WO2021259390A2 - Coronary artery calcified plaque detection method and apparatus - Google Patents

Coronary artery calcified plaque detection method and apparatus Download PDF

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WO2021259390A2
WO2021259390A2 PCT/CN2021/122142 CN2021122142W WO2021259390A2 WO 2021259390 A2 WO2021259390 A2 WO 2021259390A2 CN 2021122142 W CN2021122142 W CN 2021122142W WO 2021259390 A2 WO2021259390 A2 WO 2021259390A2
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area
gray
organ
target
lesion
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French (fr)
Chinese (zh)
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WO2021259390A3 (en
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梁隆恺
吴振洲
刘盼
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北京安德医智科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G06T5/30—Erosion or dilatation, e.g. thinning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • the present disclosure relates to the technical field of computer vision, and in particular to a method and device for detecting coronary calcified plaques.
  • Cardiovascular disease is a disease that seriously endangers human health. Most cardiovascular diseases are caused by atherosclerotic plaques. Calcified plaque in the coronary arteries is a sign of the degree of atherosclerosis, which can predict the possibility of coronary artery disease in patients.
  • coronary angiography images can be obtained by injecting contrast agents.
  • contrast agents due to the thickness of blood vessels, the distribution of contrast dose in the blood vessels is different, resulting in different gray values of calcified plaques.
  • the present disclosure proposes a method and device for detecting coronary calcified plaque.
  • a lesion detection method includes: segmenting an image to be processed, and determining multiple target regions of a first organ in the image to be processed; for any target region, according to The gray information of the target area determines the first gray threshold and the second gray threshold of the target area, the first gray threshold is greater than the second gray threshold; according to the first gray threshold of the target area A gray-level threshold and a second gray-level threshold respectively determine the suspected lesion area and the pseudo-lesion area in the target area; determine the lesion of the first organ according to the suspected lesion area and the pseudo-lesion area of each of the target areas area.
  • performing segmentation processing on the image to be processed and determining multiple target regions of the first organ in the image to be processed includes: segmenting the image to be processed and determining that the image is The organ area of the organ area includes a second organ and a first organ, and the size of the second organ is larger than the size of the first organ; classifying the organ area to obtain multiple classification areas; The first area of each first organ and the second area of the second organ are determined from the plurality of classification areas; according to the first area and the second area, the majority of the image to be processed is determined Target areas.
  • determining the first area of each first organ and the second area of the second organ from the multiple classification areas includes: calculating the number of pixels in the multiple classification areas The most classified area is determined as the second area; among the plurality of classified areas, the classified areas other than the second area are determined as the first area; wherein, according to the first area, A region and the second region, determining multiple target regions in the image to be processed, including: performing an expansion operation on the first region to obtain an expanded third region; from the third region , Removing the area overlapping with the second area to obtain a cropped area; performing crop processing on the image to be processed according to the cropped area to obtain the multiple target areas.
  • determining the first gray threshold value and the second gray threshold value of the target area according to the gray information of the target area includes: determining the gray value of pixels in the target area Sort, determine the gray value of the median as the second gray threshold; add the second gray threshold and a preset offset threshold to obtain the first gray threshold.
  • respectively determining the suspected lesion area and the pseudo lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area includes: according to the target area The first gray-scale threshold value of the target area is determined as the suspected lesion area; the area where the pixel in the target area has a gray value greater than the first gray-scale threshold value is determined as the suspected lesion area; according to the second gray-scale threshold value of the target area , Determining the area where the pixel in the target area has a gray value greater than the second gray threshold value as a fourth area, the fourth area includes a plurality of connected areas; the number of pixels in each connected area is greater than The connected area with the preset threshold is determined as the pseudo-focus area.
  • determining the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas includes: removing and The area where the pseudo lesion area overlaps is the lesion area of the first organ.
  • the image to be processed includes a cardiac medical image
  • the first organ includes coronary blood vessels
  • the second organ includes aortic blood vessels
  • the target area includes a coronary blood vessel segment.
  • Area, the lesion area includes a calcified plaque area.
  • a lesion detection device which includes: a segmentation module for performing segmentation processing on an image to be processed, and determining multiple target regions of a first organ in the image to be processed; and a threshold module for For any target area, determine a first gray level threshold and a second gray level threshold of the target area according to gray level information of the target area, where the first gray level threshold is greater than the second gray level threshold;
  • the suspected lesion and pseudo-lesion determination module is used to determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold and the second gray threshold of the target area;
  • the lesion determining module is used for Determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  • the segmentation module includes: a segmentation sub-module: used to segment the image to be processed and determine the organ region in the image to be processed, the organ region including the second organ and the second organ An organ, the size of the second organ is greater than the size of the first organ; classification sub-module: used to classify the organ region to obtain multiple classification regions; the first region and the second region determination sub-module: It is used to determine the first area of each first organ and the second area of the second organ from the multiple classification areas; the target area determination sub-module: is used to determine the first area and the second area according to the first area and the second area. Area, to determine multiple target areas in the image to be processed.
  • a segmentation sub-module used to segment the image to be processed and determine the organ region in the image to be processed, the organ region including the second organ and the second organ An organ, the size of the second organ is greater than the size of the first organ
  • classification sub-module used to classify the organ region to obtain multiple classification regions
  • the first region and the second region determining sub-module are configured to: determine the classification region with the largest number of pixels in the plurality of classification regions as the second region; Among the plurality of classification areas, the classification area except for the second area is determined as the first area; wherein, the target area determination sub-module is used to: perform an expansion operation on the first area to obtain the expansion After the third area; from the third area, remove the area that overlaps the second area to obtain a cropped area; perform cropping processing on the image to be processed according to the cropped area to obtain the Describe multiple target areas.
  • the threshold determination module is used to: sort the gray values of pixels in the target area, determine the gray value of the median as the second gray threshold;
  • the second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
  • the suspected lesion and pseudo-lesion determining module includes: a suspected lesion determining submodule, configured to determine the gray value of pixels in the target area greater than the first gray threshold value of the target area The area where the pixel of the first gray-scale threshold is located is determined as the suspected lesion area; the pseudo-lesion determination sub-module is configured to determine the gray-scale of the pixel in the target area according to the second gray-scale threshold of the target area.
  • the area where the pixel with the value greater than the second gray-scale threshold is determined to be the fourth area, and the fourth area includes a plurality of connected areas; the connected area with the number of pixels greater than the preset threshold in each connected area is determined as the Pseudo lesion area.
  • the lesion determination module is configured to remove the area overlapping with the pseudo-lesion area from the suspected lesion area of each target area to obtain the lesion area of the first organ.
  • an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute The above method.
  • a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the above method when executed by a processor.
  • multiple target areas can be determined from the image to be processed, and for each target area, the first grayscale threshold and the second grayscale threshold of each target area can be determined respectively, and then according to the first grayscale threshold of each target area
  • the first gray-scale threshold and the second gray-scale threshold respectively determine the suspected lesion area and the pseudo-lesion area in each target area, and finally the suspected lesion area and the pseudo-lesion area in each target area are used to determine the lesion area of the image to be processed ( Such as calcified plaque area).
  • This method can identify calcified plaques by setting different thresholds at different positions of the first organ (such as coronary vessels) in the image to be processed. Omissions increase the recall rate of calcified plaques, and can significantly improve the accuracy of calcified plaque recognition.
  • Fig. 1 shows a flowchart of a lesion detection method according to an embodiment of the present disclosure.
  • Fig. 2 shows a schematic diagram of a segmentation process of a to-be-processed image according to an embodiment of the present disclosure.
  • Fig. 3 shows a schematic diagram of a clipping region according to an embodiment of the present disclosure.
  • Fig. 4 shows a schematic diagram of grayscale distribution of a target area according to an embodiment of the present disclosure.
  • Fig. 5 shows a schematic diagram of a suspected lesion area and a pseudo-lesion area according to an embodiment of the present disclosure.
  • Fig. 6 shows a schematic diagram of a lesion area according to an embodiment of the present disclosure.
  • Fig. 7 shows a block diagram of a lesion detection device according to an embodiment of the present disclosure.
  • FIG. 8 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • FIG. 9 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • Fig. 1 shows a flow chart of a focus detection method according to an embodiment of the present disclosure.
  • the focus detection method includes: in step S1, a segmentation process is performed on an image to be processed, and the image to be processed is determined Multiple target areas of an organ.
  • step S2 for any target area, the first gray threshold and the second gray threshold of the target area are determined according to the gray information of the target area, and the first gray threshold is greater than the first gray threshold. Two gray scale threshold.
  • step S3 the suspected lesion area and the pseudo lesion area in the target area are respectively determined according to the first gray threshold value and the second gray threshold value of the target area.
  • step S4 the lesion area of the first organ is determined according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  • the lesion detection method can be executed by electronic equipment such as a terminal device or a server, and the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, etc., other processing equipment It can be a server or a cloud server, etc.
  • the lesion detection method can be implemented by a processor invoking a computer-readable instruction stored in a memory.
  • the method can be executed by the server.
  • the image to be processed may be a medical image, which may be an image taken by various types of medical equipment, or an image used for medical diagnosis, for example, a computer tomography (Computed Tomography). , CT) images or MRI (Magnetic Resonance Imaging, MRI) images, etc.
  • the image to be processed may be a two-dimensional medical image or a three-dimensional medical image.
  • the present disclosure does not limit the type of image to be processed and the specific acquisition method.
  • the image to be processed includes an organ and a lesion on the organ.
  • the image to be processed is a cardiac medical image
  • the organ in the image to be processed may be a blood vessel in the heart, including the aorta.
  • Blood vessels and coronary vessels, lesions on organs can be calcified plaques on coronary vessels.
  • the present disclosure does not limit the specific organ types and the types of lesions on the organs. Among them, there may be one or more lesions on the organ, and the present disclosure does not limit the number of lesions on the organ.
  • the image to be processed may be segmented to determine multiple target regions of the first organ in the image to be processed.
  • the image to be processed includes, for example, a cardiac medical image
  • the first organ in the image to be processed includes, for example, coronary blood vessels
  • the target area in the image to be processed includes, for example, the area where the coronary blood vessel segments are located.
  • a segmentation network may be preset to perform segmentation processing on the image to be processed and determine multiple target regions of the first organ in the image to be processed.
  • the segmentation network can be a deep convolutional neural network, including multiple convolutional layers, multiple deconvolutional layers, fully connected layers, etc.
  • the specific segmentation networks that can be used include but are not limited to U-Network (U-NET) , V-Network (V-NET) and other network structures, the present disclosure does not limit the specific network structure of the segmented network.
  • the gradient descent method can be used to adjust the network parameters of the segmentation network to optimize the network parameters and improve the accuracy of the segmentation network.
  • the training data can be annotated with medical image data (for example, artificially annotated coronary vascular data) by professionals with medical background under the review of senior doctors, and establish a sample image database according to a preset ratio (for example, 9:1 ) Divide each sample image into a training set and a verification set.
  • a preset ratio for example, 9:1
  • the present disclosure does not limit the value of the preset ratio.
  • the first gray-scale threshold and the second gray-scale threshold of each target area can be determined according to the gray-scale information of each target area, and the first gray-scale threshold is greater than the second gray-scale threshold. Degree threshold.
  • the first gray threshold can be used to determine the suspected lesion area in the target area
  • the second gray threshold can be used to determine the pseudo lesion area in the target area. Since the gray information of each target area may be different, the first gray threshold and the second gray threshold of each target area may also be different.
  • each target area may be segmented by the first threshold according to the first gray threshold value of each target area to determine the suspected lesion area in each target area; according to each target area The second gray-scale threshold of the region, the second threshold segmentation is performed on each target region, and the pseudo-lesion region in each target region is determined.
  • step S4 according to the suspected lesion area and the pseudo-lesion area of each of the target areas, the area of the suspected lesion area that does not contain the pseudo-lesion area is determined as the lesion area of the first organ .
  • the lesion area includes, for example, a calcified plaque area of a coronary artery.
  • multiple target areas can be determined from the image to be processed, and for each target area, the first grayscale threshold and the second grayscale threshold of each target area can be determined respectively, and then according to the first grayscale threshold of each target area
  • the first gray-scale threshold and the second gray-scale threshold determine the lesion area of the image to be processed, and different thresholds can be set for different positions of the first organ (such as coronary blood vessels) in the image to be processed to identify calcified plaques. Not only can it effectively reduce the omission of calcified plaques at the small branch vessels of the coronary blood vessels in the image to be processed, and improve the recall rate of calcified plaques, but also can significantly improve the accuracy of calcified plaque recognition.
  • step S1 may include: step S11, segmenting the image to be processed, and determining an organ region in the image to be processed, the organ region including a second organ and a first organ , The size of the second organ is larger than the size of the first organ.
  • Step S12 Classify the organ regions to obtain multiple classification regions.
  • Step S13 Determine the first area of each first organ and the second area of the second organ from the multiple classification areas.
  • Step S14 Determine multiple target areas in the image to be processed according to the first area and the second area.
  • Fig. 2 shows a schematic diagram of a segmentation process of a to-be-processed image according to an embodiment of the present disclosure.
  • the image 21 to be processed is a Computed Tomography Angiography (CTA) image
  • the image 21 to be processed is segmented to obtain a segmentation result 22, which determines the organ region in the image 21 to be processed, that is, the heart The area of blood vessels in an angiographic image.
  • the segmentation result 22 may be a binary label, marking the blood vessel area in the cardioangiography image as 1, and marking the background area outside the blood vessel area as 0.
  • the organ area is a blood vessel area, including the second organ and the first organ.
  • the second organ includes aortic vessels, that is, the organ area within the dashed frame in the segmentation result 22;
  • the first organ is a coronary vessel segment, that is, the organ area outside the dashed frame in the segmentation result 22.
  • the coronary vessels originate from the roots of the aortic vessels, and the size of the aortic vessels is larger than the size of the coronary vessels.
  • the coronary vessels are relatively small.
  • the same lesion may not affect the blood circulation in the aortic vessels, but may block the coronary vessels.
  • the brightness distribution of each branch blood vessel segment in the image to be processed is uneven. Therefore, after the organ area is obtained, it can be classified according to the size of each part on the organ.
  • step S12 the organ regions are classified to obtain multiple classification regions.
  • the organ region is a blood vessel region
  • the organ region is classified, that is, the blood vessel region is separated, and multiple blood vessel segments are obtained, and each blood vessel segment is a classification area.
  • the neural network algorithm can be used to input the organ region into a pre-trained classification network to obtain multiple classification regions; or, the organ region can be circumscribed by a rectangle. Cutting is performed to obtain a plurality of classification regions; among them, the plurality of classification regions obtained by the cutting can be respectively input into a preset first organ registration model for registration prediction, and the positional relationship between the classification regions is obtained.
  • step S13 may include: determining the classification area with the largest number of pixels in the plurality of classification areas as the second area; dividing the plurality of classification areas by The classification area outside the second area is determined to be the first area.
  • the organ area includes N classification areas, that is, classification area 1 to classification area N. Calculate the number of pixels in each of the N classification areas, determine the classification area with the largest number of pixels as the second area, and determine the remaining N-1 classification areas as the first area.
  • the organ area is the blood vessel area of the segmentation result 22 in FIG. Area
  • the first area is the branch blood vessel segment area outside the dashed box in the segmentation result 22 (ie, the coronary blood vessel area), including multiple branch blood vessel segment areas.
  • step S14 may include: performing an expansion operation on the first region to obtain a third region after expansion; from the third region, removing the region overlapping with the second region , Obtain a cropped area; perform crop processing on the image to be processed according to the cropped area to obtain the multiple target areas.
  • Fig. 3 shows a schematic diagram of a clipping region according to an embodiment of the present disclosure.
  • V other represents the first area (the solid rectangular area in Figure 3), which is the coronary vascular area
  • V main represents the second area (the cylinder area in Figure 3), which is the aortic vascular area .
  • the expansion operation is performed on the first area V other to obtain the expanded third area V other-big (the area in the dashed frame in FIG. 3 ).
  • the present disclosure does not limit the specific expansion operation method, and the morphological expansion operation can be performed on the first region V other ; and the first region V other can also be regionally expanded by a preset expansion coefficient.
  • first region V other in FIG. 3 uses one coronary artery segment as an illustration, and the first region V other is multiple coronary artery segment regions.
  • the shearing region V 1 is different from the second region V main. Intersection of multiple expanded coronary vascular segment regions.
  • the image to be processed is cropped to obtain multiple target areas.
  • a cropped image of the same size as the image to be processed can be determined according to the cropped area V 1.
  • the gray value of the pixels contained in the cropped area V 1 is marked as 1
  • the cropped area V The gray value of the pixels contained in the background outside 1 is marked as 0. Multiply the cropped image and the image to be processed to obtain the target area of the image to be processed.
  • the expansion operation on the first area in the organ area, it can be ensured that the calcified plaque part can be extracted together with the first area, and the recall rate and accuracy of the calcified plaque can be improved; by removing the area overlapping with the second area , It can reduce the amount of calculation and improve the efficiency of identifying calcified plaques.
  • multiple target regions of the first organ for example, coronary blood vessels
  • the first organ for example, coronary blood vessels
  • the first organ can be determined from the image to be processed, which is beneficial to subsequently setting appropriate thresholds according to the target regions of each part of the first organ, which can reduce the
  • the size of each part of the first organ is different, and the contrast dose is unevenly distributed on the first organ, which results in the influence of the different gray values of the calcified plaques on each part of the first organ, and it can also reduce the size of the first organ. Omission of calcified plaque at the site.
  • the gray level threshold of each target area can be determined according to the gray information of each target area.
  • step S2 may include: sorting the gray values of pixels in the target area, determining the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
  • FIG. 4 shows a schematic diagram of the grayscale distribution of a target area according to an embodiment of the present disclosure.
  • the pixels in any target area can be sorted according to the size of the gray value, and the abscissa in the figure represents the target area
  • the gray value of the middle pixel is A to B, and the vertical axis represents the number of pixels corresponding to the gray value.
  • the gray value range A to B of the pixel in the target area can be determined according to the image type of the image to be processed. For example, assuming that the image to be processed is a CT image, the gray value of the pixel in the target area (that is, the CT value ) The range is -1000HU ⁇ 1000HU, and HU is the Heinz unit of the CT value. The present disclosure does not limit the value range of the gray value.
  • the median gray value T 0 can be determined as the second gray threshold, and the second gray threshold T 0 is added to the preset offset threshold T 1 to obtain the first gray Degree threshold T fin .
  • the result of the addition of the second gray-scale threshold T 0 and the preset offset threshold T 1 needs to be less than the maximum gray-scale value of the pixel in the target area.
  • the preset offset threshold T 1 can be determined based on clinical experience. The specific value of the offset threshold T 1 is not limited.
  • first gray threshold T fin and second gray level can be determined according to the gray information of each target area.
  • Degree threshold T 0 can be determined according to the gray information of each target area.
  • different thresholds can be set for different positions of the first organ (for example, coronary blood vessels), reduce the influence of the inconsistency of brightness at different positions in the first organ, and improve the accuracy of calcified plaque recognition.
  • step S2 After the first gray-scale threshold and the second gray-scale threshold of each target area are determined in step S2, the suspected lesion area and the pseudo-lesion area of each target area can be determined through step S3.
  • step S3 may include: according to the first gray-scale threshold of the target area, determining the area where the pixel in the target area has a gray-scale value greater than the first gray-scale threshold, Determined as the suspected lesion area.
  • the second gray-scale threshold of the target area determine the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as a fourth area, and the fourth area includes a plurality of Connected areas: Connected areas where the number of pixels in each connected area is greater than a preset threshold are determined as the pseudo-focus area.
  • Fig. 5 shows a schematic diagram of a suspected lesion area and a pseudo-lesion area according to an embodiment of the present disclosure.
  • the cylindrical shape represents a target area.
  • the first gray threshold of the target area perform the first threshold segmentation process on the target area, and the gray value of the pixels in the target area is greater than the first gray scale.
  • the area where the threshold pixel is located is determined as the suspected lesion area 51 (the black solid line area in FIG. 5).
  • the fourth area 52 may include a plurality of connected areas, and a connected area with a pixel number greater than a preset threshold in each connected area is determined as a pseudo-focus area.
  • the fourth area in Figure 5 includes 3 connected domains, the number of pixels in the two gray dotted areas is less than the preset threshold, and the number of pixels in the black dotted area is greater than the preset threshold.
  • the black dotted area can be determined as Pseudo lesion area.
  • the preset threshold can be determined based on clinical experience.
  • the lesion is a calcified plaque on a coronary artery, which can be set to the maximum value of calcified plaque statistics based on clinical experience.
  • the present disclosure does not limit the specific value of the preset threshold. .
  • the process of determining the suspected lesion area and the pseudo-lesion area from one target area can be referred to in FIG. 5, and the suspected lesion area and the pseudo-lesion area of each target area can be determined respectively.
  • the suspected lesion area determined according to the second gray-scale threshold can increase the recall rate of calcified plaques
  • the pseudo-lesion area determined according to the first gray-scale threshold for example, the small branch blood vessels on the coronary vessels are high.
  • the bright part can reduce the false positive rate of calcified plaques. Therefore, by determining the suspected lesion area and the pseudo-lesion area, it is helpful to improve the accuracy of subsequent calcified plaque determination.
  • step S3 the suspected lesion area and the pseudo-lesion area of each target area are determined, and the lesion area of the first organ (for example, the calcified plaque area of coronary blood vessels) can be determined through step S4.
  • the lesion area of the first organ for example, the calcified plaque area of coronary blood vessels
  • step S4 may include: removing the area overlapping with the pseudo-lesion area from the suspected lesion area of each of the target areas to obtain the lesion area of the first organ (for example, coronary artery). Calcified plaque areas of blood vessels).
  • Fig. 6 shows a schematic diagram of a lesion area according to an embodiment of the present disclosure.
  • the cylindrical shape represents a target area
  • the solid line area represents the suspected lesion area 51
  • the dotted line area represents the pseudo lesion area 61 (including the gray area 51).
  • the area (gray area) overlapping with the pseudo lesion area 61 is removed to obtain the lesion area (black area).
  • determining the lesion area (such as the calcified plaque area) based on the suspected lesion area and the pseudo-lesion area is beneficial to increase the recall rate of calcified plaques, reduce the false positive rate of calcified plaques, and thus improve the recognition of calcified plaques Accuracy.
  • multiple target regions can be determined from the image to be processed, and for each target region, the first grayscale threshold and the second grayscale threshold of each target region can be determined respectively, and then according to The first gray-scale threshold and the second gray-scale threshold of each target area respectively determine the suspected lesion area and the pseudo-lesion area in each target area. Finally, the suspected lesion area and the pseudo-lesion area in each target area can be used to determine the target area. Process the image of the lesion area, such as the calcified plaque area.
  • the method is simple and easy to implement, not only can effectively reduce the omission of calcified plaques at the small branch vessels of the coronary blood vessels in the image to be processed, and improve the recall rate of calcified plaques, but also can significantly improve the accuracy of calcified plaque recognition.
  • the present disclosure also provides lesion detection devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the lesion detection methods provided in the present disclosure.
  • lesion detection devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the lesion detection methods provided in the present disclosure.
  • FIG. 7 shows a block diagram of a lesion detection device according to an embodiment of the present disclosure.
  • the device includes: a segmentation module 71, configured to perform segmentation processing on an image to be processed, and determine the first organ in the image to be processed Multiple target areas.
  • the threshold determination module 72 is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the second gray-scale threshold is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the second gray-scale threshold is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the second gray-scale threshold is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the suspected lesion and pseudo-lesion determining module 73 is configured to respectively determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area.
  • the lesion determination module 74 is configured to determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  • the segmentation module 71 includes: a segmentation sub-module: used to segment the image to be processed and determine an organ region in the image to be processed, and the organ region includes a second organ and The size of the first organ, the second organ is larger than the size of the first organ.
  • Classification sub-module used to classify the organ regions to obtain multiple classification regions.
  • the first area and second area determining sub-module used to determine the first area of each first organ and the second area of the second organ from the multiple classification areas.
  • Target area determination sub-module used to determine multiple target areas in the image to be processed according to the first area and the second area.
  • the first region and the second region determining sub-module are configured to: determine the classification region with the largest number of pixels in the plurality of classification regions as the second region; Among the plurality of classification areas, classification areas other than the second area are determined as the first area.
  • the target area determining submodule is used to: perform an expansion operation on the first area to obtain a third area after expansion; from the third area, remove the area overlapping with the second area to obtain Cropping area; performing cropping processing on the image to be processed according to the cropping area to obtain the multiple target areas.
  • the threshold determination module 72 is used to: sort the gray values of pixels in the target area, determine the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
  • the suspected lesion and pseudo-lesion determining module 73 includes: a suspected lesion determining sub-module, configured to calculate the gray value of pixels in the target area according to the first gray level threshold of the target area The area where the pixel is larger than the first gray-scale threshold is determined as the suspected lesion area.
  • the pseudo-lesion determination sub-module is configured to determine, according to the second gray-scale threshold of the target area, the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as the fourth area,
  • the fourth area includes a plurality of connected areas; a connected area whose number of pixels in each connected area is greater than a preset threshold is determined as the pseudo-focus area.
  • the lesion determination module 74 is configured to remove the area overlapping with the pseudo-lesion area from the suspected lesion area of each of the target areas to obtain the lesion area of the first organ.
  • the image to be processed includes a cardiac medical image
  • the first organ includes coronary blood vessels
  • the second organ includes aortic blood vessels
  • the target area includes a coronary blood vessel segment.
  • Area, the lesion area includes a calcified plaque area.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.
  • the electronic device can be provided as a terminal, server or other form of device.
  • FIG. 8 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
  • the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
  • the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
  • the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
  • the memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic Disk Magnetic Disk or Optical Disk.
  • the power supply component 806 provides power for various components of the electronic device 800.
  • the power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor can not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
  • the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
  • the component is the display and the keypad of the electronic device 800.
  • the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
  • the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
  • the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor component 814 may also include a light sensor, such as a complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) image sensor, for use in imaging applications.
  • CMOS complementary metal oxide semiconductor
  • CCD charge coupled device
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access a wireless network based on communication standards, such as wireless network (WiFi), second-generation mobile communication technology (2G), third-generation mobile communication technology (3G), fourth-generation mobile communication technology (4G) or The fifth-generation mobile communication technology (5G), or a combination of them.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field-available A programmable gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to implement the above method.
  • FIG. 9 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • the electronic device 1900 may be provided as a server.
  • the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs.
  • the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-mentioned method.
  • the electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on the operating system stored in the memory 1932, such as the Microsoft Server Operating System (Windows ServerTM), the graphical user interface operating system (Mac OS XTM) launched by Apple, and the multi-user and multi-process computer operating system (UnixTM) ), free and open source Unix-like operating system (LinuxTM), open source Unix-like operating system (FreeBSDTM) or similar.
  • Windows ServerTM Microsoft Server Operating System
  • Mac OS XTM graphical user interface operating system
  • UnixTM multi-user and multi-process computer operating system
  • LinuxTM free and open source Unix-like operating system
  • FreeBSDTM open source Unix-like operating system
  • a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the user’s computer) connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by using the status information of the computer-readable program instructions.
  • FPGA field programmable gate array
  • PDA programmable logic array
  • the computer-readable program instructions are executed to realize various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagram can represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function.
  • Executable instructions can be included in the blocks in the flowchart or block diagram.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the computer program product can be specifically implemented by hardware, software, or a combination thereof.
  • the computer program product is specifically embodied as a computer storage medium.
  • the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc. Wait.
  • SDK software development kit

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Abstract

The present disclosure relates to a coronary artery calcified plaque detection method and apparatus. The method comprises: segmenting an image to be processed, and determining a plurality of target regions of a first organ in said image; for any target region, determining a first grayscale threshold value and a second grayscale threshold value of the target region according to grayscale information of the target region; respectively determining a suspected lesion region and a pseudo lesion region in the target region according to the first grayscale threshold value and the second grayscale threshold value of the target region; and determining a lesion region of the first organ according to the suspected lesion regions and pseudo lesion regions of the target regions. According to the embodiments of the present disclosure, not only can the omission of calcified plaque at small branch blood vessels of a coronary artery blood vessel in an image to be processed be effectively reduced, but the recall rate of calcified plaque is also improved, and the accuracy of the identification of calcified plaque can be significantly improved.

Description

一种冠脉钙化斑块检测方法及装置Method and device for detecting coronary artery calcified plaque 技术领域Technical field
本公开涉及计算机视觉技术领域，尤其涉及一种冠脉钙化斑块检测方法及装置。The present disclosure relates to the technical field of computer vision, and in particular to a method and device for detecting coronary calcified plaques.
背景技术Background technique
心血管疾病是严重危害人类健康的一种疾病，大多数的心血管疾病是由动脉粥样硬化斑块导致的。冠脉内的钙化斑块是发生动脉粥样硬化程度大小的一个标志，可以对患者发生冠脉疾病的可能性做出预测。Cardiovascular disease is a disease that seriously endangers human health. Most cardiovascular diseases are caused by atherosclerotic plaques. Calcified plaque in the coronary arteries is a sign of the degree of atherosclerosis, which can predict the possibility of coronary artery disease in patients.
在临床中，可通过注射造影剂增强的方式获得冠脉血管造影图像，然而由于血管的粗细不同，造影剂量在血管中分布的剂量不同，从而导致钙化斑块显示的灰度值也不同。In clinical practice, coronary angiography images can be obtained by injecting contrast agents. However, due to the thickness of blood vessels, the distribution of contrast dose in the blood vessels is different, resulting in different gray values of calcified plaques.
发明内容Summary of the invention
有鉴于此，本公开提出了一种冠脉钙化斑块检测方法及装置。In view of this, the present disclosure proposes a method and device for detecting coronary calcified plaque.
根据本公开的一方面，提供了一种病灶检测方法，所述方法包括：对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域；针对任一目标区域，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，所述第一灰度阈值大于所述第二灰度阈值；根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域；根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域。According to one aspect of the present disclosure, there is provided a lesion detection method, the method includes: segmenting an image to be processed, and determining multiple target regions of a first organ in the image to be processed; for any target region, according to The gray information of the target area determines the first gray threshold and the second gray threshold of the target area, the first gray threshold is greater than the second gray threshold; according to the first gray threshold of the target area A gray-level threshold and a second gray-level threshold respectively determine the suspected lesion area and the pseudo-lesion area in the target area; determine the lesion of the first organ according to the suspected lesion area and the pseudo-lesion area of each of the target areas area.
在一种可能的实现方式中，对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域，包括：对所述待处理图像进行分割，确定所述待处理图像中的器官区域，所述器官区域中包括第二器官和第一器官，所述第二器官的尺寸大于所述第一器官的尺寸；对所述器官区域进行分类，得到多个分类区域；从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域；根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域。In a possible implementation manner, performing segmentation processing on the image to be processed and determining multiple target regions of the first organ in the image to be processed includes: segmenting the image to be processed and determining that the image is The organ area of the organ area includes a second organ and a first organ, and the size of the second organ is larger than the size of the first organ; classifying the organ area to obtain multiple classification areas; The first area of each first organ and the second area of the second organ are determined from the plurality of classification areas; according to the first area and the second area, the majority of the image to be processed is determined Target areas.
在一种可能的实现方式中，从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域，包括：将所述多个分类区域中像素数量最多的分类区域，确定为所述第二区域；将所述多个分类区域中，除所述第二区域之外的分类区域，确定为所述第一区域；其中，所述根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域，包括：对所述第一区域进行膨胀操作，得到膨胀后的第三区域；从所述第三区域中，去除与所述第二区域重叠的区域，得到剪切区域；根据所述剪切区域，对所述待处理图像进行剪切处理，得到所述多个目标区域。In a possible implementation, determining the first area of each first organ and the second area of the second organ from the multiple classification areas includes: calculating the number of pixels in the multiple classification areas The most classified area is determined as the second area; among the plurality of classified areas, the classified areas other than the second area are determined as the first area; wherein, according to the first area, A region and the second region, determining multiple target regions in the image to be processed, including: performing an expansion operation on the first region to obtain an expanded third region; from the third region , Removing the area overlapping with the second area to obtain a cropped area; performing crop processing on the image to be processed according to the cropped area to obtain the multiple target areas.
在一种可能的实现方式中，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，包括：对所述目标区域中像素的灰度值进行排序，将中位数的灰度值确定为所述第二灰度阈值；将所述第二灰度阈值与预设偏移阈值相加，得到 所述第一灰度阈值。In a possible implementation manner, determining the first gray threshold value and the second gray threshold value of the target area according to the gray information of the target area includes: determining the gray value of pixels in the target area Sort, determine the gray value of the median as the second gray threshold; add the second gray threshold and a preset offset threshold to obtain the first gray threshold.
在一种可能的实现方式中，根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域，包括：根据所述目标区域的第一灰度阈值，将所述目标区域中像素的灰度值大于所述第一灰度阈值的像素所在区域，确定为所述疑似病灶区域；根据所述目标区域的第二灰度阈值，将所述目标区域中像素的灰度值大于所述第二灰度阈值的像素所在区域，确定为第四区域，所述第四区域包括多个连通区域；将各连通区域中像素数量大于预设阈值的连通区域，确定为所述伪病灶区域。In a possible implementation manner, respectively determining the suspected lesion area and the pseudo lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area includes: according to the target area The first gray-scale threshold value of the target area is determined as the suspected lesion area; the area where the pixel in the target area has a gray value greater than the first gray-scale threshold value is determined as the suspected lesion area; according to the second gray-scale threshold value of the target area , Determining the area where the pixel in the target area has a gray value greater than the second gray threshold value as a fourth area, the fourth area includes a plurality of connected areas; the number of pixels in each connected area is greater than The connected area with the preset threshold is determined as the pseudo-focus area.
在一种可能的实现方式中，根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域，包括：从各个所述目标区域的疑似病灶区域中，去除与所述伪病灶区域重叠的区域，得到所述第一器官的病灶区域。In a possible implementation manner, determining the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas includes: removing and The area where the pseudo lesion area overlaps is the lesion area of the first organ.
在一种可能的实现方式中，所述待处理图像包括心脏医学图像，所述第一器官包括冠脉血管，所述第二器官包括主动脉血管，所述目标区域包括冠脉血管段所在的区域，所述病灶区域包括钙化斑块区域。In a possible implementation, the image to be processed includes a cardiac medical image, the first organ includes coronary blood vessels, the second organ includes aortic blood vessels, and the target area includes a coronary blood vessel segment. Area, the lesion area includes a calcified plaque area.
根据本公开的一方面，提供了一种病灶检测装置，包括：分割模块，用于对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域；阈值模块，用于针对任一目标区域，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，所述第一灰度阈值大于所述第二灰度阈值；疑似病灶及伪病灶确定模块，用于根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域；病灶确定模块，用于根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域。According to one aspect of the present disclosure, there is provided a lesion detection device, which includes: a segmentation module for performing segmentation processing on an image to be processed, and determining multiple target regions of a first organ in the image to be processed; and a threshold module for For any target area, determine a first gray level threshold and a second gray level threshold of the target area according to gray level information of the target area, where the first gray level threshold is greater than the second gray level threshold; The suspected lesion and pseudo-lesion determination module is used to determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold and the second gray threshold of the target area; the lesion determining module is used for Determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
在一种可能的实现方式中，分割模块包括：分割子模块：用于对所述待处理图像进行分割，确定所述待处理图像中的器官区域，所述器官区域中包括第二器官和第一器官，所述第二器官的尺寸大于所述第一器官的尺寸；分类子模块：用于对所述器官区域进行分类，得到多个分类区域；第一区域及第二区域确定子模块：用于从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域；目标区域确定子模块：用于根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域。In a possible implementation, the segmentation module includes: a segmentation sub-module: used to segment the image to be processed and determine the organ region in the image to be processed, the organ region including the second organ and the second organ An organ, the size of the second organ is greater than the size of the first organ; classification sub-module: used to classify the organ region to obtain multiple classification regions; the first region and the second region determination sub-module: It is used to determine the first area of each first organ and the second area of the second organ from the multiple classification areas; the target area determination sub-module: is used to determine the first area and the second area according to the first area and the second area. Area, to determine multiple target areas in the image to be processed.
在一种可能的实现方式中，所述第一区域及第二区域确定子模块用于：将所述多个分类区域中像素数量最多的分类区域，确定为所述第二区域；将所述多个分类区域中，除所述第二区域之外的分类区域，确定为所述第一区域；其中，所述目标区域确定子模块用于：对所述第一区域进行膨胀操作，得到膨胀后的第三区域；从所述第三区域中，去除与所述第二区域重叠的区域，得到剪切区域；根据所述剪切区域，对所述待处理图像进行剪切处理，得到所述多个目标区域。In a possible implementation manner, the first region and the second region determining sub-module are configured to: determine the classification region with the largest number of pixels in the plurality of classification regions as the second region; Among the plurality of classification areas, the classification area except for the second area is determined as the first area; wherein, the target area determination sub-module is used to: perform an expansion operation on the first area to obtain the expansion After the third area; from the third area, remove the area that overlaps the second area to obtain a cropped area; perform cropping processing on the image to be processed according to the cropped area to obtain the Describe multiple target areas.
在一种可能的实现方式中，阈值确定模块用于：对所述目标区域中像素的灰度值进行排序，将中位数的灰度值确定为所述第二灰度阈值；将所述第二灰度阈值与预设偏移阈值相加，得到所述第一灰度阈值。In a possible implementation manner, the threshold determination module is used to: sort the gray values of pixels in the target area, determine the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
在一种可能的实现方式中，疑似病灶及伪病灶确定模块包括：疑似病灶确定子模块，用于根据所述目标区域的第一灰度阈值，将所述目标区域中像素的灰度值大于所述第一灰度阈值的像素所在区域，确定为所述疑似病灶区域；伪病灶确定子模块，用于根据所述目标区域的第二灰度阈值，将所述目标区域中像素的灰度值大于所述第二灰度阈值的像素所在区域，确定为第四区域，所述第四区域包括多个连通区域；将各连通区域中像素数量大于预设阈值的连通区域，确定为所述伪病灶区域。In a possible implementation manner, the suspected lesion and pseudo-lesion determining module includes: a suspected lesion determining submodule, configured to determine the gray value of pixels in the target area greater than the first gray threshold value of the target area The area where the pixel of the first gray-scale threshold is located is determined as the suspected lesion area; the pseudo-lesion determination sub-module is configured to determine the gray-scale of the pixel in the target area according to the second gray-scale threshold of the target area. The area where the pixel with the value greater than the second gray-scale threshold is determined to be the fourth area, and the fourth area includes a plurality of connected areas; the connected area with the number of pixels greater than the preset threshold in each connected area is determined as the Pseudo lesion area.
在一种可能的实现方式中，病灶确定模块用于：从各个所述目标区域的疑似病灶区域中，去除与所述伪病灶区域重叠的区域，得到所述第一器官的病灶区域。In a possible implementation manner, the lesion determination module is configured to remove the area overlapping with the pseudo-lesion area from the suspected lesion area of each target area to obtain the lesion area of the first organ.
根据本公开的另一方面，提供了一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述的方法。According to another aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute The above method.
根据本公开的另一方面，提供了一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。According to another aspect of the present disclosure, there is provided a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the above method when executed by a processor.
在本公开实施例中，能够从待处理图像中确定多个目标区域，并针对各个目标区域，分别确定各个目标区域的第一灰度阈值和第二灰度阈值，再根据各个目标区域的第一灰度阈值和第二灰度阈值，分别确定各个目标区域中的疑似病灶区域及伪病灶区域，最后通过各个目标区域中的疑似病灶区域及伪病灶区域，确定出待处理图像的病灶区域(例如钙化斑块区域)。该方法可通过对待处理图像第一器官(例如冠脉血管)的不同位置设置不同的阈值进行钙化斑块的识别，不仅能够有效减少待处理图像中冠脉血管的细小分支血管处钙化斑块的遗漏，提升了钙化斑块的召回率，而且能够显著提高钙化斑块识别的准确度。In the embodiment of the present disclosure, multiple target areas can be determined from the image to be processed, and for each target area, the first grayscale threshold and the second grayscale threshold of each target area can be determined respectively, and then according to the first grayscale threshold of each target area The first gray-scale threshold and the second gray-scale threshold respectively determine the suspected lesion area and the pseudo-lesion area in each target area, and finally the suspected lesion area and the pseudo-lesion area in each target area are used to determine the lesion area of the image to be processed ( Such as calcified plaque area). This method can identify calcified plaques by setting different thresholds at different positions of the first organ (such as coronary vessels) in the image to be processed. Omissions increase the recall rate of calcified plaques, and can significantly improve the accuracy of calcified plaque recognition.
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。It should be understood that the above general description and the following detailed description are only exemplary and explanatory, rather than limiting the present disclosure. According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear.
附图说明Description of the drawings
包含在说明书中并且构成说明书的一部分的附图与说明书一起示出了本公开的示例性实施例、特征和方面，并且用于解释本公开的原理。The drawings included in the specification and constituting a part of the specification together with the specification illustrate exemplary embodiments, features, and aspects of the present disclosure, and are used to explain the principle of the present disclosure.
图1示出根据本公开实施例的病灶检测方法的流程图。Fig. 1 shows a flowchart of a lesion detection method according to an embodiment of the present disclosure.
图2示出根据本公开实施例的待处理图像分割处理的示意图。Fig. 2 shows a schematic diagram of a segmentation process of a to-be-processed image according to an embodiment of the present disclosure.
图3示出根据本公开实施例的剪切区域的示意图。Fig. 3 shows a schematic diagram of a clipping region according to an embodiment of the present disclosure.
图4示出根据本公开实施例的目标区域灰度分布的示意图。Fig. 4 shows a schematic diagram of grayscale distribution of a target area according to an embodiment of the present disclosure.
图5示出根据本公开实施例的疑似病灶区域和伪病灶区域的示意图。Fig. 5 shows a schematic diagram of a suspected lesion area and a pseudo-lesion area according to an embodiment of the present disclosure.
图6示出根据本公开实施例的病灶区域的示意图。Fig. 6 shows a schematic diagram of a lesion area according to an embodiment of the present disclosure.
图7示出根据本公开实施例的病灶检测装置的框图。Fig. 7 shows a block diagram of a lesion detection device according to an embodiment of the present disclosure.
图8示出根据本公开实施例的电子设备的框图。FIG. 8 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
图9示出根据本公开实施例的电子设备的框图。FIG. 9 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
具体实施方式detailed description
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。Hereinafter, various exemplary embodiments, features, and aspects of the present disclosure will be described in detail with reference to the drawings. The same reference numerals in the drawings indicate elements with the same or similar functions. Although various aspects of the embodiments are shown in the drawings, unless otherwise noted, the drawings are not necessarily drawn to scale.
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。The dedicated word "exemplary" here means "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" need not be construed as being superior or better than other embodiments.
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。The term "and/or" in this article is only an association relationship that describes the associated objects, which means that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, exist alone B these three situations. In addition, the term "at least one" in this document means any one of a plurality of or any combination of at least two of the plurality, for example, including at least one of A, B, and C, and may mean including A, Any one or more elements selected in the set formed by B and C.
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present disclosure can also be implemented without certain specific details. In some instances, the methods, means, elements, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the gist of the present disclosure.
图1示出根据本公开实施例的病灶检测方法的流程图，如图1所示，所述病灶检测方法包括：在步骤S1中，对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域。Fig. 1 shows a flow chart of a focus detection method according to an embodiment of the present disclosure. As shown in Fig. 1, the focus detection method includes: in step S1, a segmentation process is performed on an image to be processed, and the image to be processed is determined Multiple target areas of an organ.
在步骤S2中，针对任一目标区域，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，所述第一灰度阈值大于所述第二灰度阈值。In step S2, for any target area, the first gray threshold and the second gray threshold of the target area are determined according to the gray information of the target area, and the first gray threshold is greater than the first gray threshold. Two gray scale threshold.
在步骤S3中，根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域。In step S3, the suspected lesion area and the pseudo lesion area in the target area are respectively determined according to the first gray threshold value and the second gray threshold value of the target area.
在步骤S4中，根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域。In step S4, the lesion area of the first organ is determined according to the suspected lesion area and the pseudo lesion area of each of the target areas.
在一种可能的实现方式中，所述病灶检测方法可以由终端设备或服务器等电子设备执行，终端设备可以为用户设备(User Equipment，UE)、移动设备、用户终端、终端等，其它处理设备可为服务器或云端服务器等。在一些可能的实现方式中，该病灶检测方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。或者，可通过服务器执行该方法。In a possible implementation, the lesion detection method can be executed by electronic equipment such as a terminal device or a server, and the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, etc., other processing equipment It can be a server or a cloud server, etc. In some possible implementations, the lesion detection method can be implemented by a processor invoking a computer-readable instruction stored in a memory. Alternatively, the method can be executed by the server.
在一种可能的实现方式中，待处理图像可为医学图像，该医学图像可以是各种类型的医疗设备拍摄的图像，或者，用于医疗诊断的图像，例如，电子计算机断层扫描(Computed Tomography，CT)图像或者核磁共振(Magnetic Resonance Imaging，MRI)图像等。其中，待处理图像可为二维医学图像、或三维医学图像。本公开对待处理图像的类型及具体获取方式不作限制。In a possible implementation, the image to be processed may be a medical image, which may be an image taken by various types of medical equipment, or an image used for medical diagnosis, for example, a computer tomography (Computed Tomography). , CT) images or MRI (Magnetic Resonance Imaging, MRI) images, etc. The image to be processed may be a two-dimensional medical image or a three-dimensional medical image. The present disclosure does not limit the type of image to be processed and the specific acquisition method.
在一种可能的实现方式中，所述待处理图像包括器官、以及器官上的病灶，例如所述待处理图像为心脏医学图像，待处理图像中的器官可以是心脏中的血管，包括主动脉血管和冠脉血管，器官上的病灶可以是冠脉血管上的钙化斑块。本公开对具体的器官类型和器官上的病灶类型不作限制。其中，器官上的病灶可以有一个或多个，本公开对器官上的病灶个数不作限制。In a possible implementation, the image to be processed includes an organ and a lesion on the organ. For example, the image to be processed is a cardiac medical image, and the organ in the image to be processed may be a blood vessel in the heart, including the aorta. Blood vessels and coronary vessels, lesions on organs can be calcified plaques on coronary vessels. The present disclosure does not limit the specific organ types and the types of lesions on the organs. Among them, there may be one or more lesions on the organ, and the present disclosure does not limit the number of lesions on the organ.
在一种可能的实现方式中，在步骤S1中，可对待处理图像进行分割处理，确定待处理图像中第一器官的多个目标区域。其中，待处理图像例如包括心脏医学图像，待处理图像中的第一器官例如包括冠脉血管，待处理图像中的目标区域例如包括冠脉血管段所在的区域。In a possible implementation manner, in step S1, the image to be processed may be segmented to determine multiple target regions of the first organ in the image to be processed. The image to be processed includes, for example, a cardiac medical image, the first organ in the image to be processed includes, for example, coronary blood vessels, and the target area in the image to be processed includes, for example, the area where the coronary blood vessel segments are located.
在一种可能的实现方式中，可预先设置分割网络，用于对待处理图像进行分割处理，确定待处理图像中第一器官的多个目标区域。分割网络可以是深度卷积神经网络，包括多个卷积层、多个反卷积层、全连接层等，具体可采用的分割网络包括并不限于U形网络(U Network，U-NET)、V形网络(V Network，V-NET)等网络结构，本公开对分割网络的具体网络结构不作限制。In a possible implementation manner, a segmentation network may be preset to perform segmentation processing on the image to be processed and determine multiple target regions of the first organ in the image to be processed. The segmentation network can be a deep convolutional neural network, including multiple convolutional layers, multiple deconvolutional layers, fully connected layers, etc. The specific segmentation networks that can be used include but are not limited to U-Network (U-NET) , V-Network (V-NET) and other network structures, the present disclosure does not limit the specific network structure of the segmented network.
其中，可设置一个随机数作为网络参数的初始值，通过训练集的样本图像训练该网络的参数，再由验证集检测该网络的分割误差，调整该网络的参数，重复上述过程，直到该网络在验证集上误差最小，得到训练好的分割网络。其中，可以通过梯度下降法来调节分割网络的网络参数，使得网络参数优化，提升分割网络的准确率。Among them, you can set a random number as the initial value of the network parameters, train the parameters of the network through the sample images of the training set, and then detect the segmentation error of the network from the verification set, adjust the parameters of the network, and repeat the above process until the network The error is the smallest on the verification set, and a trained segmentation network is obtained. Among them, the gradient descent method can be used to adjust the network parameters of the segmentation network to optimize the network parameters and improve the accuracy of the segmentation network.
其中，训练数据可由具有医学背景的专业人士在高年资医生的审核下标注医学图像数据(例如，人工标注的冠脉血管数据)，建立样本图像库，并按照预设比例(例如9:1)将各样本图像划分为训练集和验证集，本公开对预设比例的取值不作限定。Among them, the training data can be annotated with medical image data (for example, artificially annotated coronary vascular data) by professionals with medical background under the review of senior doctors, and establish a sample image database according to a preset ratio (for example, 9:1 ) Divide each sample image into a training set and a verification set. The present disclosure does not limit the value of the preset ratio.
在一种可能的实现方式中，在步骤S2中，可根据各个目标区域的灰度信息，确定各个目标区域的第一灰度阈值和第二灰度阈值，第一灰度阈值大于第二灰度阈值。In a possible implementation manner, in step S2, the first gray-scale threshold and the second gray-scale threshold of each target area can be determined according to the gray-scale information of each target area, and the first gray-scale threshold is greater than the second gray-scale threshold. Degree threshold.
其中，第一灰度阈值可用于确定目标区域中的疑似病灶区域，第二灰度阈值可用于确定目标区域中的伪病灶区域。由于各目标区域的灰度信息可能各不相同，各目标区域的第一灰度阈值和第二灰度阈值也可各不相同。The first gray threshold can be used to determine the suspected lesion area in the target area, and the second gray threshold can be used to determine the pseudo lesion area in the target area. Since the gray information of each target area may be different, the first gray threshold and the second gray threshold of each target area may also be different.
在一种可能的实现方式中，在步骤S3中，可根据各目标区域的第一灰度阈值，对各目标区域分别进行第一阈值分割，确定各目标区域中的疑似病灶区域；根据各目标区域的第二灰度阈值，对各目标区域分别进行第二阈值分割，确定各目标区域中的伪病灶区域。In a possible implementation manner, in step S3, each target area may be segmented by the first threshold according to the first gray threshold value of each target area to determine the suspected lesion area in each target area; according to each target area The second gray-scale threshold of the region, the second threshold segmentation is performed on each target region, and the pseudo-lesion region in each target region is determined.
在一种可能的实现方式中，在步骤S4中，根据各个所述目标区域的疑似病灶区域及伪病灶区域，将疑似病灶区域中不包含伪病灶区域的区域，确定为第一器官的病灶区域。其中，所述病灶区域例如包括冠脉血管的钙化斑块区域。In a possible implementation manner, in step S4, according to the suspected lesion area and the pseudo-lesion area of each of the target areas, the area of the suspected lesion area that does not contain the pseudo-lesion area is determined as the lesion area of the first organ . Wherein, the lesion area includes, for example, a calcified plaque area of a coronary artery.
根据本公开的实施例，能够从待处理图像中确定多个目标区域，并针对各个目标区域，分别确定各个目标区域的第一灰度阈值和第二灰度阈值，再根据各个目标区域的第一灰度阈值和第二灰度阈值，确定出待处理图像的病灶区域，也即可通过对待处理图像 第一器官(例如冠脉血管)的不同位置设置不同的阈值进行钙化斑块的识别，不仅能够有效减少待处理图像中冠脉血管的细小分支血管处钙化斑块的遗漏，提升了钙化斑块的召回率，而且能够显著提高钙化斑块识别的准确度。According to the embodiments of the present disclosure, multiple target areas can be determined from the image to be processed, and for each target area, the first grayscale threshold and the second grayscale threshold of each target area can be determined respectively, and then according to the first grayscale threshold of each target area The first gray-scale threshold and the second gray-scale threshold determine the lesion area of the image to be processed, and different thresholds can be set for different positions of the first organ (such as coronary blood vessels) in the image to be processed to identify calcified plaques. Not only can it effectively reduce the omission of calcified plaques at the small branch vessels of the coronary blood vessels in the image to be processed, and improve the recall rate of calcified plaques, but also can significantly improve the accuracy of calcified plaque recognition.
下面对根据本公开实施例的病灶检测方法进行展开说明。The following is an expanded description of the lesion detection method according to the embodiment of the present disclosure.
在一种可能的实现方式中，步骤S1可包括：步骤S11，对所述待处理图像进行分割，确定所述待处理图像中的器官区域，所述器官区域中包括第二器官和第一器官，所述第二器官的尺寸大于所述第一器官的尺寸。In a possible implementation, step S1 may include: step S11, segmenting the image to be processed, and determining an organ region in the image to be processed, the organ region including a second organ and a first organ , The size of the second organ is larger than the size of the first organ.
步骤S12，对所述器官区域进行分类，得到多个分类区域。Step S12: Classify the organ regions to obtain multiple classification regions.
步骤S13，从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域。Step S13: Determine the first area of each first organ and the second area of the second organ from the multiple classification areas.
步骤S14，根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域。Step S14: Determine multiple target areas in the image to be processed according to the first area and the second area.
图2示出根据本公开实施例的待处理图像分割处理的示意图。如图2所示，待处理图像21为心脏血管造影图像(Computed Tomography Angiography，CTA)，对待处理图像21进行分割处理，得到分割结果22，确定出待处理图像21中的器官区域，也即心脏血管造影图像中的血管区域。其中，分割结果22可以是二值标签，将心脏血管造影图像中的血管区域标记为1，将血管区域以外的背景区域标记为0。Fig. 2 shows a schematic diagram of a segmentation process of a to-be-processed image according to an embodiment of the present disclosure. As shown in FIG. 2, the image 21 to be processed is a Computed Tomography Angiography (CTA) image, and the image 21 to be processed is segmented to obtain a segmentation result 22, which determines the organ region in the image 21 to be processed, that is, the heart The area of blood vessels in an angiographic image. The segmentation result 22 may be a binary label, marking the blood vessel area in the cardioangiography image as 1, and marking the background area outside the blood vessel area as 0.
如分割结果22所示，器官区域为血管区域，包括第二器官和第一器官。其中，第二器官包括主动脉血管，即分割结果22中虚线框内的器官区域；第一器官为冠脉血管段，即分割结果22中虚线框外的器官区域。As shown in the segmentation result 22, the organ area is a blood vessel area, including the second organ and the first organ. Among them, the second organ includes aortic vessels, that is, the organ area within the dashed frame in the segmentation result 22; the first organ is a coronary vessel segment, that is, the organ area outside the dashed frame in the segmentation result 22.
如分割结果22所示，冠脉血管起源于主动脉血管的根部，主动脉血管的尺寸大于冠脉血管的尺寸。对比主动脉血管，冠脉血管比较细小，相同的病灶(钙化斑块)可能对主动脉血管内血液的流通没有影响，却可能堵塞冠脉血管。并且，受到造影剂的剂量分布以及血管粗细的影响，待处理图像中各分支血管段亮度分布不均，因此，在得到器官区域后，可按照器官上各部位的尺寸进行分类。As shown in the segmentation result 22, the coronary vessels originate from the roots of the aortic vessels, and the size of the aortic vessels is larger than the size of the coronary vessels. Compared with the aortic vessels, the coronary vessels are relatively small. The same lesion (calcified plaque) may not affect the blood circulation in the aortic vessels, but may block the coronary vessels. Moreover, affected by the dose distribution of the contrast agent and the thickness of the blood vessel, the brightness distribution of each branch blood vessel segment in the image to be processed is uneven. Therefore, after the organ area is obtained, it can be classified according to the size of each part on the organ.
在一种可能的实现方式中，在步骤S12中，对所述器官区域进行分类，得到多个分类区域。In a possible implementation manner, in step S12, the organ regions are classified to obtain multiple classification regions.
举例来说，假设器官区域为血管区域，对器官区域进行分类处理，也即对血管区域进行分离处理，得到多个血管段，每个血管段为一个分类区域。For example, assuming that the organ region is a blood vessel region, the organ region is classified, that is, the blood vessel region is separated, and multiple blood vessel segments are obtained, and each blood vessel segment is a classification area.
应当理解，本公开对具体的分类方法不作限制，可利用神经网络算法，将器官区域输入预先训练好的分类网络，得到多个分类区域；或者，可按照器官区域外接矩形的方式，对器官区域进行切割，得到多个分类区域；其中，可将切割获得的多个分类区域，分别输入预设的第一器官配准模型进行配准预测，得到分类区域间的位置关系。It should be understood that the present disclosure does not limit the specific classification method. The neural network algorithm can be used to input the organ region into a pre-trained classification network to obtain multiple classification regions; or, the organ region can be circumscribed by a rectangle. Cutting is performed to obtain a plurality of classification regions; among them, the plurality of classification regions obtained by the cutting can be respectively input into a preset first organ registration model for registration prediction, and the positional relationship between the classification regions is obtained.
在一种可能的实现方式中，步骤S13中可包括：将所述多个分类区域中像素数量最多的分类区域，确定为所述第二区域；将所述多个分类区域中，除所述第二区域之外的分类区域，确定为所述第一区域。In a possible implementation manner, step S13 may include: determining the classification area with the largest number of pixels in the plurality of classification areas as the second area; dividing the plurality of classification areas by The classification area outside the second area is determined to be the first area.
举例来说，假设器官区域包括N个分类区域，即分类区域1～分类区域N。对N个分类区域分别计算各自区域内的像素数量，将像素数量最多的分类区域确定为第二区域，将其余N-1个分类区域分别确定为第一区域。For example, suppose that the organ area includes N classification areas, that is, classification area 1 to classification area N. Calculate the number of pixels in each of the N classification areas, determine the classification area with the largest number of pixels as the second area, and determine the remaining N-1 classification areas as the first area.
例如，器官区域为图2的分割结果22的血管区域，器官区域包括的N个分类区域也即N个血管段，第二区域为分割结果22中虚线框中的血管段区域(即主动脉血管区域)，第一区域为分割结果22中虚线框外的分支血管段区域(即冠脉血管区域)，包括多个分支血管段区域。For example, the organ area is the blood vessel area of the segmentation result 22 in FIG. Area), the first area is the branch blood vessel segment area outside the dashed box in the segmentation result 22 (ie, the coronary blood vessel area), including multiple branch blood vessel segment areas.
因此，通过将多个分类区域划分为各个第一器官的第一区域及第二器官的第二区域，有利于更精准的确定后续的目标区域，减少计算量，提高计算效率。Therefore, by dividing the multiple classification areas into the first area of each first organ and the second area of the second organ, it is beneficial to more accurately determine the subsequent target area, reduce the amount of calculation, and improve the calculation efficiency.
在一种可能的实现方式中，步骤S14可包括：对所述第一区域进行膨胀操作，得到膨胀后的第三区域；从所述第三区域中，去除与所述第二区域重叠的区域，得到剪切区域；根据所述剪切区域，对所述待处理图像进行剪切处理，得到所述多个目标区域。In a possible implementation manner, step S14 may include: performing an expansion operation on the first region to obtain a third region after expansion; from the third region, removing the region overlapping with the second region , Obtain a cropped area; perform crop processing on the image to be processed according to the cropped area to obtain the multiple target areas.
图3示出根据本公开实施例的剪切区域的示意图。如图3所示，V other代表第一区域(图3中实线长方形区域)，也即冠脉血管区域，V main代表第二区域(图3中圆柱体区域)，也即主动脉血管区域。 Fig. 3 shows a schematic diagram of a clipping region according to an embodiment of the present disclosure. As shown in Figure 3, V other represents the first area (the solid rectangular area in Figure 3), which is the coronary vascular area, and V main represents the second area (the cylinder area in Figure 3), which is the aortic vascular area .
对第一区域V other进行膨胀操作，得到膨胀后的第三区域V other-big(图3中虚线框区域)。其中，本公开对具体膨胀操作的方法不作限制，可对第一区域V other进行形态学膨胀操作；也可以通过预设膨胀系数，对第一区域V other进行区域扩展。 The expansion operation is performed on the first area V other to obtain the expanded third area V other-big (the area in the dashed frame in FIG. 3 ). Among them, the present disclosure does not limit the specific expansion operation method, and the morphological expansion operation can be performed on the first region V other ; and the first region V other can also be regionally expanded by a preset expansion coefficient.
从第三区域V other-big中，去除与第二区域V main重叠的区域(图3中条纹区域)，得到剪切区域V 1(图3中灰色区域)。 From the third area V other-big , the area overlapping with the second area V main (the striped area in FIG. 3) is removed to obtain the cropped area V 1 (the gray area in FIG. 3).
应当理解，图3中第一区域V other以一个冠脉血管段作为示意，第一区域V other为多个冠脉血管段区域，对应的，剪切区域V 1为与第二区域V main无交集的多个膨胀后的冠脉血管段区域。 It should be understood that the first region V other in FIG. 3 uses one coronary artery segment as an illustration, and the first region V other is multiple coronary artery segment regions. Correspondingly, the shearing region V 1 is different from the second region V main. Intersection of multiple expanded coronary vascular segment regions.
根据剪切区域V 1，对待处理图像进行剪切处理，得到多个目标区域。例如，可根据剪切区域V 1确定与待处理图像等尺寸的剪切图像，在剪切图像中，将剪切区域V 1所包含的像素的灰度值标记为1，将剪切区域V 1外的背景所包含的像素的灰度值标记为0。将剪切图像和待处理图像相乘，得到待处理图像的目标区域。 According to the cropping area V 1 , the image to be processed is cropped to obtain multiple target areas. For example, a cropped image of the same size as the image to be processed can be determined according to the cropped area V 1. In the cropped image, the gray value of the pixels contained in the cropped area V 1 is marked as 1, and the cropped area V The gray value of the pixels contained in the background outside 1 is marked as 0. Multiply the cropped image and the image to be processed to obtain the target area of the image to be processed.
因此，通过对器官区域中的第一区域进行膨胀操作，可确保钙化斑块部分能够与第一区域一起提取出来，提高钙化斑块的召回率和精准度；通过去除与第二区域重叠的区域，可减少计算量，提高钙化斑块的识别效率。Therefore, by performing the expansion operation on the first area in the organ area, it can be ensured that the calcified plaque part can be extracted together with the first area, and the recall rate and accuracy of the calcified plaque can be improved; by removing the area overlapping with the second area , It can reduce the amount of calculation and improve the efficiency of identifying calcified plaques.
通过步骤S11～步骤S14，可从待处理图像中确定出第一器官(例如冠脉血管)的多个目标区域，有利于后续根据第一器官各部位的目标区域设置合适的阈值，可减少由于第一器官各部位尺寸不同，造影剂量在第一器官上分布不均，所导致的第一器官上各部位钙化斑块的灰度值不同带来的影响，而且还可以减少第一器官上细小部位处钙化斑块的遗漏。Through steps S11 to S14, multiple target regions of the first organ (for example, coronary blood vessels) can be determined from the image to be processed, which is beneficial to subsequently setting appropriate thresholds according to the target regions of each part of the first organ, which can reduce the The size of each part of the first organ is different, and the contrast dose is unevenly distributed on the first organ, which results in the influence of the different gray values of the calcified plaques on each part of the first organ, and it can also reduce the size of the first organ. Omission of calcified plaque at the site.
在步骤S1得到多个目标区域后，可分别根据各个目标区域的灰度信息，确定各个目 标区域的灰度阈值。After obtaining multiple target areas in step S1, the gray level threshold of each target area can be determined according to the gray information of each target area.
在一种可能的实现方式中，步骤S2可包括：对所述目标区域中像素的灰度值进行排序，将中位数的灰度值确定为所述第二灰度阈值；将所述第二灰度阈值与预设偏移阈值相加，得到所述第一灰度阈值。In a possible implementation manner, step S2 may include: sorting the gray values of pixels in the target area, determining the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
图4示出根据本公开实施例的目标区域灰度分布的示意图，如图4所示，可将任一目标区域中的像素，按照灰度值的大小进行排序，图中横坐标表示目标区域中像素的灰度值A～B，纵轴表示对应灰度值的像素数量。FIG. 4 shows a schematic diagram of the grayscale distribution of a target area according to an embodiment of the present disclosure. As shown in FIG. 4, the pixels in any target area can be sorted according to the size of the gray value, and the abscissa in the figure represents the target area The gray value of the middle pixel is A to B, and the vertical axis represents the number of pixels corresponding to the gray value.
应当理解，目标区域中像素的灰度值的取值范围A～B可根据待处理图像的图像类型确定，例如假设待处理图像为CT图像，目标区域中像素的灰度值(也即CT值)范围为-1000HU～1000HU，HU为CT值的亨氏单位，本公开对灰度值的取值范围不作限制。It should be understood that the gray value range A to B of the pixel in the target area can be determined according to the image type of the image to be processed. For example, assuming that the image to be processed is a CT image, the gray value of the pixel in the target area (that is, the CT value ) The range is -1000HU～1000HU, and HU is the Heinz unit of the CT value. The present disclosure does not limit the value range of the gray value.
如图4所示，可将中位数的灰度值T 0确定为所述第二灰度阈值，将第二灰度阈值T 0与预设偏移阈值T 1相加，得到第一灰度阈值T fin。其中，第二灰度阈值T 0与预设偏移阈值T 1相加结果需小于目标区域中像素的最大灰度值，预设偏移阈值T 1可根据临床经验确定，本公开对预设偏移阈值T 1的具体数值不作限制。 As shown in Figure 4, the median gray value T 0 can be determined as the second gray threshold, and the second gray threshold T 0 is added to the preset offset threshold T 1 to obtain the first gray Degree threshold T fin . Wherein, the result of the addition of the second gray-scale threshold T 0 and the preset offset threshold T 1 needs to be less than the maximum gray-scale value of the pixel in the target area. The preset offset threshold T 1 can be determined based on clinical experience. The specific value of the offset threshold T 1 is not limited.
应当理解，在待处理图像中可包括多个目标区域，各个目标区域的灰度信息各不相同，可根据各个目标区域的灰度信息分别确定对应的第一灰度阈值T fin和第二灰度阈值T 0。 It should be understood that multiple target areas may be included in the image to be processed, and the gray information of each target area is different. The corresponding first gray threshold T fin and second gray level can be determined according to the gray information of each target area. Degree threshold T 0 .
通过这种方式，可以对第一器官(例如冠脉血管)的不同位置设置不同的阈值，减少第一器官中不同位置亮度不一致带来的影响，提高钙化斑块识别的精准度。In this way, different thresholds can be set for different positions of the first organ (for example, coronary blood vessels), reduce the influence of the inconsistency of brightness at different positions in the first organ, and improve the accuracy of calcified plaque recognition.
在步骤S2确定了各个目标区域的第一灰度阈值和第二灰度阈值后，可通过步骤S3确定各个目标区域的疑似病灶区域及伪病灶区域。After the first gray-scale threshold and the second gray-scale threshold of each target area are determined in step S2, the suspected lesion area and the pseudo-lesion area of each target area can be determined through step S3.
在一种可能的实现方式中，步骤S3可包括：根据所述目标区域的第一灰度阈值，将所述目标区域中像素的灰度值大于所述第一灰度阈值的像素所在区域，确定为所述疑似病灶区域。In a possible implementation manner, step S3 may include: according to the first gray-scale threshold of the target area, determining the area where the pixel in the target area has a gray-scale value greater than the first gray-scale threshold, Determined as the suspected lesion area.
根据所述目标区域的第二灰度阈值，将所述目标区域中像素的灰度值大于所述第二灰度阈值的像素所在区域，确定为第四区域，所述第四区域包括多个连通区域；将各连通区域中像素数量大于预设阈值的连通区域，确定为所述伪病灶区域。According to the second gray-scale threshold of the target area, determine the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as a fourth area, and the fourth area includes a plurality of Connected areas: Connected areas where the number of pixels in each connected area is greater than a preset threshold are determined as the pseudo-focus area.
图5示出根据本公开实施例的疑似病灶区域和伪病灶区域的示意图。如图5所示，圆柱形表示一个目标区域，根据该目标区域的第一灰度阈值，对该目标区域进行第一阈值分割处理，将该目标区域中像素的灰度值大于第一灰度阈值的像素所在区域，确定为疑似病灶区域51(图5中黑色实线区域)。Fig. 5 shows a schematic diagram of a suspected lesion area and a pseudo-lesion area according to an embodiment of the present disclosure. As shown in Figure 5, the cylindrical shape represents a target area. According to the first gray threshold of the target area, perform the first threshold segmentation process on the target area, and the gray value of the pixels in the target area is greater than the first gray scale. The area where the threshold pixel is located is determined as the suspected lesion area 51 (the black solid line area in FIG. 5).
根据该目标区域的第二灰度阈值，对该目标区域进行第二阈值分割处理，将该目标区域中像素的灰度值大于所述第二灰度阈值的像素所在区域，确定为第四区域52(图5中虚线区域，图中标识的虚线区域包括51区域)。Perform a second threshold segmentation process on the target area according to the second grayscale threshold of the target area, and determine the area where the pixels in the target area have a grayscale value greater than the second grayscale threshold as the fourth area 52 (the dotted area in FIG. 5, the dotted area identified in the figure includes area 51).
第四区域52可包括多个连通区域，将各连通区域中像素数量大于预设阈值的连通区域，确定为伪病灶区域。例如，图5中的第四区域包括了3个连通域，2个灰色的虚线区域中像素数量小于预设阈值，黑色的虚线区域中像素数量大于预设阈值，可将黑色的虚线 区域确定为伪病灶区域。The fourth area 52 may include a plurality of connected areas, and a connected area with a pixel number greater than a preset threshold in each connected area is determined as a pseudo-focus area. For example, the fourth area in Figure 5 includes 3 connected domains, the number of pixels in the two gray dotted areas is less than the preset threshold, and the number of pixels in the black dotted area is greater than the preset threshold. The black dotted area can be determined as Pseudo lesion area.
其中，预设阈值可根据临床经验确定，例如病灶为冠脉血管上的钙化斑块，可根据临床经验将其设置为钙化斑块统计的最大值，本公开对预设阈值的具体数值不作限制。Among them, the preset threshold can be determined based on clinical experience. For example, the lesion is a calcified plaque on a coronary artery, which can be set to the maximum value of calcified plaque statistics based on clinical experience. The present disclosure does not limit the specific value of the preset threshold. .
应当理解，对于多个目标区域，可参考图5从一个目标区域中确定疑似病灶区域和伪病灶区域的过程，分别确定各个目标区域的疑似病灶区域和伪病灶区域。It should be understood that, for multiple target areas, the process of determining the suspected lesion area and the pseudo-lesion area from one target area can be referred to in FIG. 5, and the suspected lesion area and the pseudo-lesion area of each target area can be determined respectively.
通过这种方式，根据第二灰度阈值确定的疑似病灶区域，可提高钙化斑块的召回率，根据第一灰度阈值确定的伪病灶区域(例如，冠脉血管上细小分支血管处的高亮部分)，可降低钙化斑块的假阳率，因此，通过确定疑似病灶区域及伪病灶区域，有利于提高后续钙化斑块确定的精准度。In this way, the suspected lesion area determined according to the second gray-scale threshold can increase the recall rate of calcified plaques, and the pseudo-lesion area determined according to the first gray-scale threshold (for example, the small branch blood vessels on the coronary vessels are high). The bright part) can reduce the false positive rate of calcified plaques. Therefore, by determining the suspected lesion area and the pseudo-lesion area, it is helpful to improve the accuracy of subsequent calcified plaque determination.
在步骤S3确定了各个目标区域的疑似病灶区域及伪病灶区域，可通过步骤S4确定第一器官的病灶区域(例如冠脉血管的钙化斑块区域)。In step S3, the suspected lesion area and the pseudo-lesion area of each target area are determined, and the lesion area of the first organ (for example, the calcified plaque area of coronary blood vessels) can be determined through step S4.
在一种可能实现的方式中，步骤S4可包括：从各个所述目标区域的疑似病灶区域中，去除与所述伪病灶区域重叠的区域，得到所述第一器官的病灶区域(例如冠脉血管的钙化斑块区域)。In a possible implementation manner, step S4 may include: removing the area overlapping with the pseudo-lesion area from the suspected lesion area of each of the target areas to obtain the lesion area of the first organ (for example, coronary artery). Calcified plaque areas of blood vessels).
图6示出根据本公开实施例的病灶区域的示意图。如图6所示，圆柱形表示一个目标区域，实线区域代表疑似病灶区域51，虚线区域代表伪病灶区域61(包括灰色的51区域)。在疑似病灶区域51中，去除与伪病灶区域61重叠的区域(灰色区域)，得到病灶区域(黑色区域)。Fig. 6 shows a schematic diagram of a lesion area according to an embodiment of the present disclosure. As shown in FIG. 6, the cylindrical shape represents a target area, the solid line area represents the suspected lesion area 51, and the dotted line area represents the pseudo lesion area 61 (including the gray area 51). In the suspected lesion area 51, the area (gray area) overlapping with the pseudo lesion area 61 is removed to obtain the lesion area (black area).
应当理解，对于多个目标区域，可参考图6从一个目标区域中确定病灶区域的过程，分别从各个目标区域的疑似病灶区域中，确定各个目标区域的病灶区域，各个目标区域的病灶区域的集合，也即第一器官的病灶区域。It should be understood that for multiple target areas, refer to the process of determining the lesion area from one target area with reference to FIG. 6, and determine the lesion area of each target area from the suspected lesion area of each target area, and the lesion area of each target area. Collection, that is, the lesion area of the first organ.
通过这种方式，根据疑似病灶区域和伪病灶区域确定病灶区域(例如钙化斑块区域)，有利于提高钙化斑块的召回率，降低钙化斑块的假阳率，从而可提高钙化斑块识别的精准度。In this way, determining the lesion area (such as the calcified plaque area) based on the suspected lesion area and the pseudo-lesion area is beneficial to increase the recall rate of calcified plaques, reduce the false positive rate of calcified plaques, and thus improve the recognition of calcified plaques Accuracy.
因此，根据本公开实施例的病灶检测方法，能够从待处理图像中确定多个目标区域，并针对各个目标区域，分别确定各个目标区域的第一灰度阈值和第二灰度阈值，再根据各个目标区域的第一灰度阈值和第二灰度阈值，分别确定各个目标区域中的疑似病灶区域及伪病灶区域，最后可通过各个目标区域中的疑似病灶区域及伪病灶区域，确定出待处理图像的病灶区域，例如钙化斑块区域。该方法简单易于实现，不仅能够有效减少待处理图像中冠脉血管的细小分支血管处钙化斑块的遗漏，提升了钙化斑块的召回率，而且能够显著提高钙化斑块识别的准确度。Therefore, according to the lesion detection method of the embodiment of the present disclosure, multiple target regions can be determined from the image to be processed, and for each target region, the first grayscale threshold and the second grayscale threshold of each target region can be determined respectively, and then according to The first gray-scale threshold and the second gray-scale threshold of each target area respectively determine the suspected lesion area and the pseudo-lesion area in each target area. Finally, the suspected lesion area and the pseudo-lesion area in each target area can be used to determine the target area. Process the image of the lesion area, such as the calcified plaque area. The method is simple and easy to implement, not only can effectively reduce the omission of calcified plaques at the small branch vessels of the coronary blood vessels in the image to be processed, and improve the recall rate of calcified plaques, but also can significantly improve the accuracy of calcified plaque recognition.
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。It can be understood that, without violating the principle logic, the various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment, which is limited in length and will not be repeated in this disclosure. Those skilled in the art can understand that, in the above method of the specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.
此外，本公开还提供了病灶检测装置、电子设备、计算机可读存储介质、程序，上 述均可用来实现本公开提供的任一种病灶检测方法，相应技术方案和描述和参见方法部分的相应记载，不再赘述。In addition, the present disclosure also provides lesion detection devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the lesion detection methods provided in the present disclosure. For the corresponding technical solutions and descriptions, refer to the corresponding records in the method section. ,No longer.
图7示出根据本公开实施例的病灶检测装置的框图，如图7所示，所述装置包括：分割模块71，用于对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域。FIG. 7 shows a block diagram of a lesion detection device according to an embodiment of the present disclosure. As shown in FIG. 7, the device includes: a segmentation module 71, configured to perform segmentation processing on an image to be processed, and determine the first organ in the image to be processed Multiple target areas.
阈值确定模块72，用于针对任一目标区域，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，所述第一灰度阈值大于所述第二灰度阈值。The threshold determination module 72 is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information. The second gray-scale threshold.
疑似病灶及伪病灶确定模块73，用于根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域。The suspected lesion and pseudo-lesion determining module 73 is configured to respectively determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area.
病灶确定模块74，用于根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域。The lesion determination module 74 is configured to determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
在一种可能的实现方式中，分割模块71包括：分割子模块：用于对所述待处理图像进行分割，确定所述待处理图像中的器官区域，所述器官区域中包括第二器官和第一器官，所述第二器官的尺寸大于所述第一器官的尺寸。In a possible implementation, the segmentation module 71 includes: a segmentation sub-module: used to segment the image to be processed and determine an organ region in the image to be processed, and the organ region includes a second organ and The size of the first organ, the second organ is larger than the size of the first organ.
分类子模块：用于对所述器官区域进行分类，得到多个分类区域。Classification sub-module: used to classify the organ regions to obtain multiple classification regions.
第一区域及第二区域确定子模块：用于从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域。The first area and second area determining sub-module: used to determine the first area of each first organ and the second area of the second organ from the multiple classification areas.
目标区域确定子模块：用于根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域。Target area determination sub-module: used to determine multiple target areas in the image to be processed according to the first area and the second area.
在一种可能的实现方式中，所述第一区域及第二区域确定子模块用于：将所述多个分类区域中像素数量最多的分类区域，确定为所述第二区域；将所述多个分类区域中，除所述第二区域之外的分类区域，确定为所述第一区域。In a possible implementation manner, the first region and the second region determining sub-module are configured to: determine the classification region with the largest number of pixels in the plurality of classification regions as the second region; Among the plurality of classification areas, classification areas other than the second area are determined as the first area.
其中，所述目标区域确定子模块用于：对所述第一区域进行膨胀操作，得到膨胀后的第三区域；从所述第三区域中，去除与所述第二区域重叠的区域，得到剪切区域；根据所述剪切区域，对所述待处理图像进行剪切处理，得到所述多个目标区域。Wherein, the target area determining submodule is used to: perform an expansion operation on the first area to obtain a third area after expansion; from the third area, remove the area overlapping with the second area to obtain Cropping area; performing cropping processing on the image to be processed according to the cropping area to obtain the multiple target areas.
在一种可能的实现方式中，阈值确定模块72用于：对所述目标区域中像素的灰度值进行排序，将中位数的灰度值确定为所述第二灰度阈值；将所述第二灰度阈值与预设偏移阈值相加，得到所述第一灰度阈值。In a possible implementation manner, the threshold determination module 72 is used to: sort the gray values of pixels in the target area, determine the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
在一种可能的实现方式中，疑似病灶及伪病灶确定模块73包括：疑似病灶确定子模块，用于根据所述目标区域的第一灰度阈值，将所述目标区域中像素的灰度值大于所述第一灰度阈值的像素所在区域，确定为所述疑似病灶区域。In a possible implementation manner, the suspected lesion and pseudo-lesion determining module 73 includes: a suspected lesion determining sub-module, configured to calculate the gray value of pixels in the target area according to the first gray level threshold of the target area The area where the pixel is larger than the first gray-scale threshold is determined as the suspected lesion area.
伪病灶确定子模块，用于根据所述目标区域的第二灰度阈值，将所述目标区域中像素的灰度值大于所述第二灰度阈值的像素所在区域，确定为第四区域，所述第四区域包括多个连通区域；将各连通区域中像素数量大于预设阈值的连通区域，确定为所述伪病灶区域。The pseudo-lesion determination sub-module is configured to determine, according to the second gray-scale threshold of the target area, the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as the fourth area, The fourth area includes a plurality of connected areas; a connected area whose number of pixels in each connected area is greater than a preset threshold is determined as the pseudo-focus area.
在一种可能的实现方式中，病灶确定模块74用于：从各个所述目标区域的疑似病灶 区域中，去除与所述伪病灶区域重叠的区域，得到所述第一器官的病灶区域。In a possible implementation manner, the lesion determination module 74 is configured to remove the area overlapping with the pseudo-lesion area from the suspected lesion area of each of the target areas to obtain the lesion area of the first organ.
在一种可能的实现方式中，所述待处理图像包括心脏医学图像，所述第一器官包括冠脉血管，所述第二器官包括主动脉血管，所述目标区域包括冠脉血管段所在的区域，所述病灶区域包括钙化斑块区域。In a possible implementation, the image to be processed includes a cardiac medical image, the first organ includes coronary blood vessels, the second organ includes aortic blood vessels, and the target area includes a coronary blood vessel segment. Area, the lesion area includes a calcified plaque area.
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法，其具体实现可以参照上文方法实施例的描述，为了简洁，这里不再赘述。In some embodiments, the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. For specific implementation, refer to the description of the above method embodiments. For brevity, here No longer.
本公开实施例还提出一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是非易失性计算机可读存储介质。The embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor. The computer-readable storage medium may be a non-volatile computer-readable storage medium.
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.
电子设备可以被提供为终端、服务器或其它形态的设备。The electronic device can be provided as a terminal, server or other form of device.
图8示出根据本公开实施例的一种电子设备800的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等终端。FIG. 8 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
参照图8，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的接口812，传感器组件814，以及通信组件816。8, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。The processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
存储器804被配置为存储各种类型的数据以支持在电子设备800的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。The memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理***，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。The power supply component 806 provides power for various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.
多媒体组件808包括在所述电子设备800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触 摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜***或具有焦距和光学变焦能力。The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor can not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.
I/O接口812为处理组件802和***接口模块之间提供接口，上述***接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module. The above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如所述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800接触的存在或不存在，电子设备800方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如互补金属氧化物半导体(CMOS)或电荷耦合装置(CCD)图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。The sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation. For example, the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components. For example, the component is the display and the keypad of the electronic device 800. The sensor component 814 can also detect the electronic device 800 or the electronic device 800. The position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact. The sensor component 814 may also include a light sensor, such as a complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。电子设备800可以接入基于通信标准的无线网络，如无线网络(WiFi)，第二代移动通信技术(2G)，第三代移动通信技术(3G)，***移动通信技术(4G)或第五代移动通信技术(5G)，或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理***的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on communication standards, such as wireless network (WiFi), second-generation mobile communication technology (2G), third-generation mobile communication technology (3G), fourth-generation mobile communication technology (4G) or The fifth-generation mobile communication technology (5G), or a combination of them. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上 述方法。In an exemplary embodiment, there is also provided a non-volatile computer-readable storage medium, such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to implement the above method.
图9示出根据本公开实施例的一种电子设备1900的框图。例如，电子设备1900可以被提供为一服务器。参照图9，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。FIG. 9 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server. 9, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above-mentioned method.
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理，一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作***，例如微软服务器操作***(Windows ServerTM)，苹果公司推出的基于图形用户界面操作***(Mac OS XTM)，多用户多进程的计算机操作***(UnixTM),自由和开放原代码的类Unix操作***(LinuxTM)，开放原代码的类Unix操作***(FreeBSDTM)或类似。The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 . The electronic device 1900 can operate based on the operating system stored in the memory 1932, such as the Microsoft Server Operating System (Windows ServerTM), the graphical user interface operating system (Mac OS XTM) launched by Apple, and the multi-user and multi-process computer operating system (UnixTM) ), free and open source Unix-like operating system (LinuxTM), open source Unix-like operating system (FreeBSDTM) or similar.
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以完成上述方法。In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
本公开可以是***、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。The present disclosure may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是(但不限于)电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。The computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon The protruding structure in the hole card or the groove, and any suitable combination of the above. The computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸 如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。The computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages. Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the user’s computer) connect). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by using the status information of the computer-readable program instructions. The computer-readable program instructions are executed to realize various aspects of the present disclosure.
这里参照根据本公开实施例的方法、装置(***)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。Here, various aspects of the present disclosure are described with reference to flowcharts and/or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and/or block diagrams, and combinations of blocks in the flowcharts and/or block diagrams, can be implemented by computer-readable program instructions.
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。It is also possible to load computer-readable program instructions onto a computer, other programmable data processing device, or other equipment, so that a series of operation steps are executed on the computer, other programmable data processing device, or other equipment to produce a computer-implemented process , So that the instructions executed on the computer, other programmable data processing apparatus, or other equipment realize the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
附图中的流程图和框图显示了根据本公开的多个实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的***来实现，或者可以用专用硬件与计算机指令的组合来实现。The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions, and operations of the system, method, and computer program product according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function. Executable instructions. In some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中，所述计算机程序产品具体体现为计算机存储介质，在另一个可选实施例中，计算机程序产品具体体现为软件产品，例如软件开发包(Software Development Kit，SDK)等等。The computer program product can be specifically implemented by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium. In another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc. Wait.
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也 不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。The various embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Without departing from the scope and spirit of the described embodiments, many modifications and changes are obvious to those of ordinary skill in the art. The choice of terms used herein is intended to best explain the principles, practical applications, or improvements to the technology in the market for each embodiment, or to enable other ordinary skilled in the art to understand the various embodiments disclosed herein.

Claims (10)

  1. 一种病灶检测方法，其特征在于，所述方法包括：A method for detecting lesions, characterized in that the method includes:
    对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域；Performing segmentation processing on the image to be processed, and determining multiple target regions of the first organ in the image to be processed;
    针对任一目标区域，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，所述第一灰度阈值大于所述第二灰度阈值；For any target area, determine a first gray level threshold and a second gray level threshold of the target area according to gray level information of the target area, where the first gray level threshold is greater than the second gray level threshold;
    根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域；Respectively determining a suspected lesion area and a pseudo lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area;
    根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域。Determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  2. 根据权利要求1所述的方法，其特征在于，对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域，包括：The method according to claim 1, wherein performing segmentation processing on the image to be processed and determining multiple target regions of the first organ in the image to be processed comprises:
    对所述待处理图像进行分割，确定所述待处理图像中的器官区域，所述器官区域中包括第二器官和第一器官，所述第二器官的尺寸大于所述第一器官的尺寸；Segmenting the image to be processed to determine an organ region in the image to be processed, the organ region includes a second organ and a first organ, and the size of the second organ is larger than the size of the first organ;
    对所述器官区域进行分类，得到多个分类区域；Classify the organ regions to obtain multiple classification regions;
    从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域；Determine the first area of each first organ and the second area of the second organ from the multiple classification areas;
    根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域。According to the first area and the second area, multiple target areas in the image to be processed are determined.
  3. 根据权利要求2所述的方法，其特征在于，从所述多个分类区域中确定出各个第一器官的第一区域及所述第二器官的第二区域，包括：The method according to claim 2, wherein determining the first area of each first organ and the second area of the second organ from the plurality of classification areas comprises:
    将所述多个分类区域中像素数量最多的分类区域，确定为所述第二区域；Determining the classification area with the largest number of pixels in the plurality of classification areas as the second area;
    将所述多个分类区域中，除所述第二区域之外的分类区域，确定为所述第一区域；Determining, among the plurality of classification areas, classification areas other than the second area as the first area;
    其中，所述根据所述第一区域及所述第二区域，确定出所述待处理图像中的多个目标区域，包括：Wherein, the determining multiple target areas in the image to be processed according to the first area and the second area includes:
    对所述第一区域进行膨胀操作，得到膨胀后的第三区域；Performing an expansion operation on the first area to obtain an expanded third area;
    从所述第三区域中，去除与所述第二区域重叠的区域，得到剪切区域；From the third area, remove the area overlapping with the second area to obtain a cropped area;
    根据所述剪切区域，对所述待处理图像进行剪切处理，得到所述多个目标区域。According to the cropping area, performing cropping processing on the image to be processed to obtain the multiple target areas.
  4. 根据权利要求1所述的方法，其特征在于，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，包括：The method according to claim 1, wherein the determining the first gray level threshold and the second gray level threshold of the target area according to the gray level information of the target area comprises:
    对所述目标区域中像素的灰度值进行排序，将中位数的灰度值确定为所述第二灰度阈值；Sorting the gray values of pixels in the target area, and determining the gray value of the median as the second gray value threshold;
    将所述第二灰度阈值与预设偏移阈值相加，得到所述第一灰度阈值。The second gray-scale threshold value and the preset offset threshold value are added to obtain the first gray-scale threshold value.
  5. 根据权利要求1所述的方法，其特征在于，根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域，包括：The method according to claim 1, wherein the respectively determining the suspected lesion area and the pseudo lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area comprises:
    根据所述目标区域的第一灰度阈值，将所述目标区域中像素的灰度值大于所述第一 灰度阈值的像素所在区域，确定为所述疑似病灶区域；Determining, according to the first gray-scale threshold value of the target area, an area where a pixel in the target area has a gray-scale value greater than the first gray-scale threshold value as the suspected lesion area;
    根据所述目标区域的第二灰度阈值，将所述目标区域中像素的灰度值大于所述第二灰度阈值的像素所在区域，确定为第四区域，所述第四区域包括多个连通区域；According to the second gray-scale threshold of the target area, determine the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as a fourth area, and the fourth area includes a plurality of Connected area
    将各连通区域中像素数量大于预设阈值的连通区域，确定为所述伪病灶区域。A connected area with a pixel number greater than a preset threshold in each connected area is determined as the pseudo-focus area.
  6. 根据权利要求1所述的方法，其特征在于，根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域，包括：The method according to claim 1, wherein determining the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas comprises:
    从各个所述目标区域的疑似病灶区域中，去除与所述伪病灶区域重叠的区域，得到所述第一器官的病灶区域。From the suspected lesion areas of each of the target regions, the area overlapping with the pseudo-lesion area is removed to obtain the lesion area of the first organ.
  7. 根据权利要求2所述的方法，其特征在于，所述待处理图像包括心脏医学图像，所述第一器官包括冠脉血管，所述第二器官包括主动脉血管，所述目标区域包括冠脉血管段所在的区域，所述病灶区域包括钙化斑块区域。The method according to claim 2, wherein the image to be processed comprises a cardiac medical image, the first organ comprises coronary blood vessels, the second organ comprises aortic blood vessels, and the target area comprises coronary arteries The area where the blood vessel segment is located, and the lesion area includes a calcified plaque area.
  8. 一种病灶检测装置，其特征在于，包括：A lesion detection device, which is characterized in that it comprises:
    分割模块，用于对待处理图像进行分割处理，确定所述待处理图像中第一器官的多个目标区域；A segmentation module, configured to perform segmentation processing on the image to be processed, and determine multiple target regions of the first organ in the image to be processed;
    阈值确定模块，用于针对任一目标区域，根据所述目标区域的灰度信息，确定所述目标区域的第一灰度阈值和第二灰度阈值，所述第一灰度阈值大于所述第二灰度阈值；Threshold determination module for determining a first gray threshold and a second gray threshold of the target area according to gray information of the target area for any target area, where the first gray threshold is greater than the gray level information of the target area. The second gray threshold;
    疑似病灶及伪病灶确定模块，用于根据所述目标区域的第一灰度阈值和第二灰度阈值，分别确定所述目标区域中的疑似病灶区域及伪病灶区域；The suspected lesion and pseudo-lesion determination module is configured to determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area;
    病灶确定模块，用于根据各个所述目标区域的疑似病灶区域及伪病灶区域，确定所述第一器官的病灶区域。The lesion determination module is configured to determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  9. 一种电子设备，其特征在于，包括：An electronic device, characterized in that it comprises:
    处理器；processor;
    用于存储处理器可执行指令的存储器；A memory for storing processor executable instructions;
    其中，所述处理器被配置为调用所述存储器存储的指令，以执行权利要求1至7中任意一项所述的方法。Wherein, the processor is configured to call instructions stored in the memory to execute the method according to any one of claims 1 to 7.
  10. 一种计算机可读存储介质，其上存储有计算机程序指令，其特征在于，所述计算机程序指令被处理器执行时实现权利要求1至7中任意一项所述的方法。A computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the method according to any one of claims 1 to 7 when the computer program instructions are executed by a processor.
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