CN111738980B - Medical image display method, computer equipment and storage medium - Google Patents
Medical image display method, computer equipment and storage medium Download PDFInfo
- Publication number
- CN111738980B CN111738980B CN202010408474.5A CN202010408474A CN111738980B CN 111738980 B CN111738980 B CN 111738980B CN 202010408474 A CN202010408474 A CN 202010408474A CN 111738980 B CN111738980 B CN 111738980B
- Authority
- CN
- China
- Prior art keywords
- value
- frequency
- parameters
- preset
- values
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
- 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
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/30061—Lung
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Radiology & Medical Imaging (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The invention mainly discloses a medical image display method, computer equipment and a storage medium. The method comprises the following steps: segmenting a lung region of a CT image of a target object; acquiring N preset parameters based on the lung region; inputting the N preset parameters of the obtained target object into an immune-related pneumonia prediction model to obtain an immune-related pneumonia prediction result; and displaying the immune-related pneumonia prediction result. By adopting the scheme provided by the invention, a doctor can quickly know whether a patient has immune-related pneumonia or not, and the diagnosis efficiency and the diagnosis accuracy of the doctor are improved to a certain extent.
Description
Technical Field
The present invention relates to the field of medical technology, and in particular, to a medical image display method, a computer device, and a computer readable storage medium.
Background
Lung cancer is one of the most frequently occurring malignant tumors. The incidence rate of the advanced lung cancer is 60-70%, and the main treatment means of the advanced lung cancer at present comprise chemotherapy, targeting and immunotherapy. For patients with EGFR, ALK, ROS isogenic mutations, targeted drug therapy can bring significant survival benefits to the patient. For patients without genetic mutations, immunotherapy is currently an important therapeutic approach, and several studies confirm the benefit of immunotherapy to the survival of patients.
The immunotherapy for clinical use is mainly based on PD1/PD-L1 immune checkpoint inhibitors, including NIVOLUMAB, PEBLIZUMAB and other medicaments, and researches show that the medicaments not only remarkably improve the survival time of patients, but also can remarkably reduce adverse reactions compared with chemotherapy.
Currently, immune checkpoint inhibitors have been widely used in clinic for the treatment of advanced non-small cell lung cancer and for consolidation treatment after radiotherapy and chemotherapy of locally advanced non-small cell lung cancer. Although the incidence of adverse reactions is low, the patient's immune adverse reactions are characterized by being unpredictable, and part of severe immune-related pneumonia is often fatal. Therefore, it is important to know whether the patient suffers from immune-related pneumonia and suggest.
Therefore, how to provide a method for displaying medical images to give whether a patient has immune-related pneumonia is one of the problems to be solved.
Disclosure of Invention
The invention provides a display method of medical images, which enables doctors to quickly know whether patients suffer from immune-related pneumonia or not, and improves diagnosis efficiency and diagnosis accuracy of doctors to a certain extent.
The invention provides a medical image display method, which comprises the following steps:
segmenting a lung region of a CT image of a target object;
acquiring N preset parameters based on the lung region;
inputting the N preset parameters of the obtained target object into an immune-related pneumonia prediction model to obtain an immune-related pneumonia prediction result;
and displaying the immune-related pneumonia prediction result.
Optionally, based on the lung region, acquiring N preset parameters includes:
generating a CT value histogram based on the lung region;
and acquiring parameters in N preset parameters at least based on the CT value histogram.
Optionally, the generating a CT value histogram based on the lung region includes:
acquiring a CT value of each voxel in the lung region;
counting the frequency of voxels corresponding to CT values in a preset CT value interval;
and generating a CT value histogram based on the CT values in the preset CT value interval and the frequency of the voxels corresponding to the CT values.
Optionally, the preset CT value interval is (-1000 hu,0 hu), and generating the CT value histogram based on the CT values in the preset CT value interval and the frequency of the voxels corresponding to the CT values includes:
dividing the preset CT value interval into K subintervals equally, wherein K is a natural number greater than or equal to 1;
obtaining the frequency number corresponding to each subinterval, wherein the frequency number corresponding to each subinterval refers to the sum of the frequency numbers of voxels corresponding to the CT values of the subinterval;
and generating a CT value histogram by taking the frequency corresponding to each subinterval as the frequency of the voxels corresponding to each CT value of the subinterval.
Optionally, the acquiring parameters in the N preset parameters at least based on the CT value histogram includes:
obtaining a standard curve;
acquiring a Hailingcase distance parameter and an IOU parameter based on a standard curve and a distribution curve of a CT value histogram;
taking the sea-ringer distance parameter and the IOU parameter as parameters in N preset parameters.
Optionally, the acquiring parameters in the N preset parameters at least based on the CT value histogram further includes:
the volume of the lung region is calculated,
calculating the ratio of the volume occupied by the voxels of M CT value intervals to the volume of the lung region respectively, wherein M is a natural number which is more than or equal to 1;
and taking M ratios as parameters in preset parameters.
Optionally, the M CT value intervals are one or more of (-1000 HU, -900 HU), (-500 HU, -400 HU), (-300 HU, -200 HU), (-200 HU, -100 HU).
Optionally, based on the lung region, acquiring N preset parameters further includes:
acquiring a CT value of each voxel in the lung region;
taking the average value, the median and the standard deviation of CT values of all voxels in the lung region as parameters in N preset parameters.
The invention also provides a display device of medical images, which comprises:
a segmentation unit for segmenting a lung region of the CT image of the target object;
the acquisition unit is used for acquiring N preset parameters based on the lung area;
the immune correlation pneumonia prediction model is used for taking N preset parameters of the acquired target object as input and outputting an immune correlation pneumonia prediction result;
and the display unit is used for displaying the immune-related pneumonia prediction result.
The invention also provides a computer device comprising at least one processor and at least one memory, wherein the memory stores a computer program, and when the program is executed by the processor, the processor is enabled to execute the medical image display method.
The invention also provides a computer readable storage medium, which when executed by a processor within a device, causes the device to perform the above-described method of displaying medical images.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the method comprises the steps of firstly dividing a lung region of a CT image of a target object, then acquiring N preset parameters based on the lung region, inputting the N preset parameters of the target object into an immune-related pneumonia prediction model to obtain a prediction result of immune-related pneumonia, and displaying the immune-related pneumonia prediction result. Firstly, input parameters of an immune-related pneumonia prediction model are acquired, and then, a prediction result of the immune-related pneumonia is output and displayed based on the input parameters and the immune-related pneumonia prediction model, so that a doctor can quickly know whether a patient suffers from the immune-related pneumonia or not, and the diagnosis efficiency and the diagnosis accuracy of the doctor are improved to a certain extent.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a method for displaying medical images according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a CT value histogram according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart illustrating a method for displaying a medical image according to an embodiment of the invention. As shown in fig. 1, the method for displaying a medical image includes the following steps:
s101, segmenting a lung region of a CT image of a target object.
S102, acquiring N preset parameters based on the lung area.
S103, inputting the N preset parameters of the obtained target object into an immune-related pneumonia prediction model to obtain an immune-related pneumonia prediction result.
S104, displaying the immune-related pneumonia prediction result.
S101 is performed to segment a lung region in a CT image of a target object. Specifically, in this embodiment, the lung region in the CT image of the segmented target object may be segmented into a lung (double lung) region by a thresholding method, a region growing method, a mean iterative segmentation method, a maximum inter-class variance segmentation method, a mode classification-based method, an image registration-based method, a shape model-based method, an AI algorithm (a three-dimensional convolutional neural network segmentation model, etc.). In the present embodiment, this is not limited as long as the lung region can be segmented from the CT image of the target object.
And S102, acquiring N preset parameters based on the lung region, wherein N is a natural number greater than or equal to 1. In this embodiment, N preset parameters may be obtained by:
first, a CT value histogram is generated based on the lung region.
In this embodiment, the CT value of each voxel in the lung region is first acquired, then the frequency of the voxels corresponding to the CT values in the preset CT value interval is counted, and finally a CT value histogram is generated based on the CT values in the preset CT value interval and the frequency of the voxels corresponding to the CT values. Those skilled in the art will recognize that the CT values corresponding to different voxels may be the same, or that the same CT value corresponds to a plurality of voxels, and that the frequency of the voxels corresponding to the CT values is the number of voxels corresponding to a certain CT value, for example, when the CT value is 100HU, the number of voxels is 12, and the frequency of the voxels corresponding to 100HU is 12. In this embodiment, the preset CT value interval may be (-1000 hu,0 hu), and the CT value histogram is generated based on the CT values in the preset CT value interval and the frequency of the voxels corresponding to the CT values, which may be specifically obtained by:
firstly, the preset CT value interval is equally divided into K subintervals, where K is a natural number greater than or equal to 1 in this embodiment. Then, the frequency number corresponding to each subinterval is obtained, wherein the frequency number corresponding to each subinterval is the sum of the frequency numbers of voxels corresponding to the CT values of the subinterval. And finally, generating a CT value histogram by taking the frequency of the voxels corresponding to the CT values of each subinterval as the frequency corresponding to the subinterval. In this embodiment, taking a preset CT value interval (-1000 HU,0 HU) as an example, the preset CT value interval is equally divided into 100 subintervals, the number of CT values included in each subinterval is 10, the number of voxels corresponding to the subinterval is (-10 HU,0 HU) as an example, if the frequency of voxels corresponding to the subinterval is 12 when the CT value is-10 HU, the frequency of voxels corresponding to the CT value is-9 HU is 15, the frequency of voxels corresponding to the CT value is-8 HU is 11, the frequency of voxels corresponding to the CT value is-7 HU is 13, the frequency of voxels corresponding to the CT value is-6 HU is 14, the frequency of voxels corresponding to the CT value is-5 HU is 12, the frequency of voxels corresponding to the CT value is-4 HU is 16, the frequency of voxels corresponding to the CT value is-3 HU is 17, the frequency of voxels corresponding to the CT value is-2 HU is 15, the frequency of voxels corresponding to the CT value is-1 HU is 12, the frequency of voxels corresponding to the CT value is-0 HU is 13, the frequency of voxels corresponding to the CT value is-10 HU, and the frequency of voxels corresponding to the CT value is 150 HU (-10 HU) can be generated as a subinterval of the corresponding subintervals of the subintervals of 150. Fig. 2 is a schematic diagram of a CT value histogram of an embodiment of the present invention, where the current curve and the light blue region covered by the current curve in fig. 2 are CT value histograms of lung regions. In fig. 2, the abscissa represents a CT value, and the ordinate represents the frequency of voxels corresponding to the CT value. In fig. 2, the CT value interval (-1000 HU,0 HU) is divided into 10 sub-intervals (-1000 HU, -900 HU), (-900 HU, -800 HU), (-800 HU, -700 HU), … …, (-100 HU,0 HU), and then each of the 10 sub-intervals is divided into 10 sub-intervals, that is, 100 sub-intervals are divided into (-1000 HU,0 HU), and the frequency of voxels corresponding to each CT value in each sub-interval is the same, that is, the frequency corresponding to the sub-interval. In this embodiment, the preset CT value interval is (-1000 hu,0 hu), and the preset CT value interval is equally divided into 100 sub-intervals, and in other embodiments, the number of the preset CT value interval and the number of the sub-intervals may be determined according to the actual clinical requirement, so that the preset CT value interval is (-1000 hu,0 hu), and the number of the sub-intervals is 100 should not be taken as a limitation of the technical solution of the present invention.
The CT value histogram of the target object lung region is generated through the above-mentioned process.
And then, acquiring parameters in N preset parameters at least based on the CT value histogram.
In this embodiment, specifically, the standard curve may be obtained. And acquiring the Hailingcase distance parameter and the IOU parameter based on the standard curve and the distribution curve of the CT value histogram. Taking the sea-ringer distance parameter and the IOU parameter as parameters in preset parameters. With continued reference to fig. 2, the baseline curve in fig. 2 is a standard curve, where the standard curve is generally obtained based on CT values of a normal human lung region, and specifically may be obtained by averaging the frequency of voxels corresponding to the same CT value among a large number of CT values of a normal human lung region. For example: when the CT value of the lung area of the target object 1 is-700, the frequency of the corresponding voxel is 50, when the CT value of the lung area of the target object 2 is-700, the frequency of the corresponding voxel is 60, when the CT value of the lung area of the target object 3 is-700, the frequency of the corresponding voxel is 50, when the CT value of the lung area of the target object 4 is-700, the frequency of the corresponding voxel is 80 and … …, when the CT value of the lung area of the target object Y is-700, the frequency of the corresponding voxel on the reference curve is the sum of the frequency of the corresponding voxels and the ratio of Y when the CT value of the target object Y is-700, and for the same CT value-700, the frequency of the corresponding voxel on the reference curve is the target object 1 and … …. After determining the average frequency of the voxels corresponding to each CT value, the standard curve can be obtained by taking the CT value as the abscissa and the average frequency of the voxels corresponding to the CT value as the ordinate.
In this embodiment, the sea-ringer distance parameter is obtained by the following formula:
wherein p is i For points on the CT value histogram curve, q i Is a point on the standard curve.
The closer the Hailin value, H (P, Q), is to 0, the higher the similarity of the CT histogram of the target subject's lung region to the standard curve distribution is indicated.
In this embodiment, the IOU parameter is obtained by the following formula:
here, area (a) is the Area under the curve of the CT value histogram, and Area (B) is the Area under the standard curve. When the IOU is closer to 1, the similarity between the CT value histogram curve and the standard curve of the lung region of the target object is higher. In this embodiment, the similarity between the CT value histogram distribution of the lung region of the target object and the standard curve can be determined by the size of the IOU.
In this embodiment, entropy, energy, kurtosis, skewness and peak of the distribution curve of the CT value histogram may also be used as parameters among preset parameters.
The entropy of the distribution curve of the CT value histogram is obtained by the following formula:
the skewness of the distribution curve of the CT value histogram is obtained by the following formula:
kurtosis of the distribution curve of the CT value histogram is obtained by the following formula:
the energy of the distribution curve of the CT value histogram is obtained by the following formula:
the peak value of the distribution curve of the CT value histogram is obtained by the following formula:
Peak＝l max
where l is the CT value of each voxel, l min For the minimum value of all CT values, l max For the maximum value of all CT values, N (l) is the number of voxels corresponding to each CT value, m is the average value of all CT values, s is the standard deviation of all CT values, N is the number of all voxels, and c is a common constant.
The volume of the lung region may be calculated, and the ratio of the volume occupied by the voxels of the M CT value intervals to the volume of the lung region may be calculated, where M is a natural number greater than or equal to 1. And taking M ratios as parameters in preset parameters. Specifically, the volume of the lung region may be determined by the number of voxels in the lung region and a preset scale. The preset scale may be empirically set. Similarly, the volume occupied by each voxel in each CT value interval is the product of the number of voxels in the CT value interval and the preset scale. The number of voxels in the CT interval can be directly obtained from the frequency number of the corresponding interval in the CT histogram. In this embodiment, M may be 1, 2, 3, 4, etc., and when M is different, the corresponding CT value interval may be one or more of (-1000 HU, -900 HU), (-500 HU, -400 HU), (-300 HU, -200 HU), (-200 HU, -100 HU). If M is 3, the CT value interval can be any three of (-1000 HU, -900 HU), (-500 HU, -400 HU), (-300 HU, -200 HU), (-200 HU, -100 HU). If M is 4, the volume occupied by the voxels of the CT value interval (-1000 HU, -900 HU), the volume occupied by the voxels of (-500 HU, -400 HU), the volume occupied by the voxels of (-300 HU, -200 HU) and the volume occupied by the voxels of (-200 HU, -100 HU) can be calculated respectively. The number of voxels corresponding to the 4 sections shown in fig. 2 is the frequency number corresponding to the section. In other embodiments, M may take different values depending on the actual clinical need.
In this embodiment, in addition to obtaining parameters of N preset parameters based at least on the CT value histogram, the N preset parameters may be obtained as follows:
specifically, a CT value is acquired for each voxel in the lung region. Taking the average value, the median and the standard deviation of CT values of all voxels in the lung region as parameters in N preset parameters.
The average of the CT values for all voxels in the lung region is obtained by the following formula:
the standard deviation of CT values for all voxels in the lung region is obtained by the following formula:
where l is the CT value of each voxel, l min For the minimum value of all CT values, l max For the maximum value of all CT values, m is the average value of all CT values, and N is the number of all voxels.
The median of the CT values of all voxels in the lung region means that the CT values of all voxels are arranged in a predetermined order (e.g., from small to large or from large to small), and the CT value of the voxel located in the middle position is taken as the median of the CT values of all voxels in the ordered queue. If the ordered queue is the 1 st to 2n+1 st voxels, the CT value of the n+1 st voxel is the median of the CT values of all voxels, and if the ordered queue is the 1 st to 2n nd voxels, the CT value of the n or n+1 th voxel is the median of the CT values of all voxels. For example, if n=4, the CT values of the 1 st to 9 th voxels are arranged in order from the smallest to the largest, and the CT value of the 5 th voxel is the median of the CT values of the 9 th voxels. If n=3, the CT values of the 3 rd or 4 th voxels are the median of the CT values of the 6 th voxels, if the 1 st to 6 th voxels are sequentially arranged in order of the CT values from the smallest.
After the N preset parameters are obtained, the N preset parameters of the target object may be input to the immune-related pneumonia prediction model to obtain a prediction result of whether the target object has immune-related pneumonia. In this embodiment, the N preset parameters of the target object obtained in the above process may be a hain lattice distance parameter of the target object, energy of a distribution curve of a CT value histogram, kurtosis of a distribution curve of a CT value histogram, entropy of a distribution curve of a CT value histogram, a ratio of a volume occupied by a voxel with a CT value of (-1000 HU, -900 HU) to a lung volume, a ratio of a volume occupied by a voxel with a CT value of (-500 HU, -400 HU) to a lung volume, a ratio of a volume occupied by a voxel with a CT value of (-300 HU, -200 HU) to a lung volume, and a ratio of a volume occupied by a voxel with a CT value of (-200 HU, -100 HU) to a lung volume. The four parameters, i.e. the sea-ringer distance parameter, the energy of the distribution curve of the CT value histogram, the kurtosis of the distribution curve of the CT value histogram and the entropy of the distribution curve of the CT value histogram, can be used for comparing the differences of the parameters of different target objects on the distribution curve of the CT value histogram, such as the form of the distribution curve, the first-order characteristics of the distribution curve and the like. The ratio of the volume occupied by the voxels in the four CT value intervals to the lung volume can reflect the difference of the lung of the target object in tissue density distribution, and in practical application, the target object can be distinguished to belong to a pneumonia group or a non-pneumonia group by comparing the 8 parameters of different target objects.
In other embodiments, the N preset parameters may be a combination of other parameters and may be determined according to actual requirements, so the above 8 preset parameters in this embodiment should not be taken as limitations on the technical solution of the present invention.
And S103, inputting the N preset parameters of the obtained target object into an immune-related pneumonia prediction model to obtain an immune-related pneumonia prediction result. In this embodiment, specifically, the 8 preset parameters of the finally obtained target object may be input to an immune-related pneumonia prediction model, so as to obtain a prediction result of whether the target object has immune-related pneumonia.
And S104, displaying the immune-related pneumonia prediction result. Specifically, the immune-related pneumonia prediction result of the target object can be displayed on any display terminal, such as a computer, an IPAD, a mobile phone, and the like.
The embodiment of the invention also provides a display device of medical images, which comprises:
and the segmentation unit is used for segmenting the lung region of the CT image of the target object.
And the acquisition unit is used for acquiring N preset parameters based on the lung region.
The immune-related pneumonia prediction model is used for taking N preset parameters of the acquired target object as input and outputting immune-related pneumonia prediction results.
And the display unit is used for displaying the immune-related pneumonia prediction result.
The implementation of the medical image display device of the present embodiment may refer to the implementation of the medical image display method described above, and will not be described herein.
Based on the same technical concept, the embodiment of the invention provides a computer device, which comprises at least one processor and at least one memory, wherein the memory stores a computer program, and when the program is executed by the processor, the processor is enabled to execute the medical image display method.
Based on the same technical idea, an embodiment of the present invention provides a computer-readable storage medium, which when executed by a processor in a device, enables the device to perform the above-described medical image display method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, or as a computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A display method of medical images is characterized in that:
segmenting a lung region of a CT image of a target object;
acquiring N preset parameters based on the lung region;
inputting the N preset parameters of the obtained target object into an immune-related pneumonia prediction model to obtain an immune-related pneumonia prediction result;
displaying the immune-related pneumonia prediction result;
based on the lung region, the acquiring N preset parameters includes:
generating a CT value histogram based on the lung region;
acquiring parameters in N preset parameters at least based on the CT value histogram;
the acquiring parameters in the N preset parameters at least based on the CT value histogram includes:
obtaining a standard curve;
acquiring a Hailingcase distance parameter and an IOU parameter based on a standard curve and a distribution curve of a CT value histogram;
taking the sea-ringer distance parameter and the IOU parameter as parameters in N preset parameters;
the standard curve is obtained based on CT values of the lung regions of normal people, specifically, the frequency of voxels corresponding to the same CT value in a large number of CT values of the lung regions of normal people is averaged, after the average frequency of the voxels corresponding to each CT value is determined, the CT value is taken as an abscissa, and the average frequency of the voxels corresponding to the CT value is taken as an ordinate to obtain the standard curve;
the sea-ringer distance parameter H (P, Q) is obtained by the following formula:
wherein p is i For points on the CT value histogram curve, q i Points on the standard curve;
the IOU parameter is obtained by the following formula:
here, area (a) is the Area under the curve of the CT value histogram, and Area (B) is the Area under the standard curve.
2. The method of claim 1, wherein the generating a CT value histogram based on the lung region comprises:
acquiring a CT value of each voxel in the lung region;
counting the frequency of voxels corresponding to CT values in a preset CT value interval;
and generating a CT value histogram based on the CT values in the preset CT value interval and the frequency of the voxels corresponding to the CT values.
3. The method of claim 2, wherein,
the preset CT value interval is (-1000 hu,0 hu), and generating the CT value histogram based on the CT values in the preset CT value interval and the frequency of the voxels corresponding to the CT values includes:
dividing the preset CT value interval into K subintervals equally, wherein K is a natural number greater than or equal to 1;
obtaining the frequency number corresponding to each subinterval, wherein the frequency number corresponding to each subinterval refers to the sum of the frequency numbers of voxels corresponding to the CT values of the subinterval;
and generating a CT value histogram by taking the frequency corresponding to each subinterval as the frequency of the voxels corresponding to each CT value of the subinterval.
4. The method of claim 1, wherein the obtaining parameters of the N preset parameters based at least on the CT value histogram further comprises:
the volume of the lung region is calculated,
calculating the ratio of the volume occupied by the voxels of M CT value intervals to the volume of the lung region respectively, wherein M is a natural number which is more than or equal to 1;
and taking M ratios as parameters in preset parameters.
5. The method of claim 4, wherein the M CT value intervals are one or more of (-1000 HU, -900 HU), (-500 HU, -400 HU), (-300 HU, -200 HU), (-200 HU, -100 HU).
6. The method of claim 1, wherein the acquiring N preset parameters based on the lung region further comprises:
acquiring a CT value of each voxel in the lung region;
taking the average value, the median and the standard deviation of CT values of all voxels in the lung region as parameters in N preset parameters.
7. A computer device comprising at least one processor and at least one memory, wherein the memory stores a computer program which, when executed by the processor, enables the processor to perform the method of displaying a medical image according to any one of claims 1 to 6.
8. A computer readable storage medium, which when executed by a processor within a device, causes the device to perform the method of displaying a medical image according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010408474.5A CN111738980B (en) | 2020-05-14 | 2020-05-14 | Medical image display method, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010408474.5A CN111738980B (en) | 2020-05-14 | 2020-05-14 | Medical image display method, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111738980A CN111738980A (en) | 2020-10-02 |
| CN111738980B true CN111738980B (en) | 2023-08-04 |
Family
ID=72647252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010408474.5A Active CN111738980B (en) | 2020-05-14 | 2020-05-14 | Medical image display method, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111738980B (en) |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008054237A1 (en) * | 2006-11-01 | 2008-05-08 | Sergey Anatolevich Demin | Method for controlling the psychophysiological state of the body |
| RU2348348C1 (en) * | 2007-10-26 | 2009-03-10 | Анатолий Сергеевич Димов | Prediction method for severe community-acquired pneumonia |
| EP2078732A1 (en) * | 2006-07-10 | 2009-07-15 | Institute for Antibodies Co., Ltd. | Method of classifying antigen, method of identifying antigen, method of obtaining antibody or antibody set, method of constructing antibody panel and antibody or antibody set and use of the same |
| DE102009051199A1 (en) * | 2009-10-29 | 2011-05-05 | Siemens Aktiengesellschaft | Device i.e. X-ray computed tomography system, for diagnosis support of physician at area of e.g. bronchial diseases of patient, has determining characteristic parameters along bronchial path and at lung area, respectively |
| CN102429679A (en) * | 2011-09-09 | 2012-05-02 | 华南理工大学 | Computer-assisted emphysema analysis system based on chest CT (Computerized Tomography) image |
| CN103310449A (en) * | 2013-06-13 | 2013-09-18 | 沈阳航空航天大学 | Lung segmentation method based on improved shape model |
| CN103442628A (en) * | 2011-03-16 | 2013-12-11 | 皇家飞利浦有限公司 | Medical instrument for examining the cervix |
| CN105469419A (en) * | 2016-01-07 | 2016-04-06 | 于翠妮 | Pulmonary nodule detection method based on CT image |
| CN107590809A (en) * | 2017-06-30 | 2018-01-16 | 上海联影医疗科技有限公司 | Lung dividing method and medical image system |
| CN109035283A (en) * | 2018-09-05 | 2018-12-18 | 中国医科大学附属盛京医院 | It is a kind of precisely to be detected and quantitative analysis method based on the pulmonary emphysema for randomly selecting subregion |
| CN109658411A (en) * | 2019-01-21 | 2019-04-19 | 杭州英库医疗科技有限公司 | A kind of correlation analysis based on CT images feature Yu Patients with Non-small-cell Lung prognosis situation |
| CN110391015A (en) * | 2019-06-14 | 2019-10-29 | 广东省人民医院(广东省医学科学院) | A method of tumor immunity is quantified based on image group |
| CN110598533A (en) * | 2019-07-31 | 2019-12-20 | 平安科技（深圳）有限公司 | Tongue picture matching method, electronic device, computer device, and storage medium |
| CN110619945A (en) * | 2018-06-19 | 2019-12-27 | 西门子医疗有限公司 | Characterization of quantities of training for input to machine learning networks |
| CN111047591A (en) * | 2020-03-13 | 2020-04-21 | 北京深睿博联科技有限责任公司 | Focal volume measuring method, system, terminal and storage medium based on deep learning |
| CN111091115A (en) * | 2019-12-31 | 2020-05-01 | 深圳中兴网信科技有限公司 | Vehicle monitoring method and device, computer equipment and storage medium |
| CN111105414A (en) * | 2019-12-31 | 2020-05-05 | 杭州依图医疗技术有限公司 | Processing method, interaction method, display method and storage medium |
-
2020
- 2020-05-14 CN CN202010408474.5A patent/CN111738980B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2078732A1 (en) * | 2006-07-10 | 2009-07-15 | Institute for Antibodies Co., Ltd. | Method of classifying antigen, method of identifying antigen, method of obtaining antibody or antibody set, method of constructing antibody panel and antibody or antibody set and use of the same |
| WO2008054237A1 (en) * | 2006-11-01 | 2008-05-08 | Sergey Anatolevich Demin | Method for controlling the psychophysiological state of the body |
| RU2348348C1 (en) * | 2007-10-26 | 2009-03-10 | Анатолий Сергеевич Димов | Prediction method for severe community-acquired pneumonia |
| DE102009051199A1 (en) * | 2009-10-29 | 2011-05-05 | Siemens Aktiengesellschaft | Device i.e. X-ray computed tomography system, for diagnosis support of physician at area of e.g. bronchial diseases of patient, has determining characteristic parameters along bronchial path and at lung area, respectively |
| CN103442628A (en) * | 2011-03-16 | 2013-12-11 | 皇家飞利浦有限公司 | Medical instrument for examining the cervix |
| CN102429679A (en) * | 2011-09-09 | 2012-05-02 | 华南理工大学 | Computer-assisted emphysema analysis system based on chest CT (Computerized Tomography) image |
| CN103310449A (en) * | 2013-06-13 | 2013-09-18 | 沈阳航空航天大学 | Lung segmentation method based on improved shape model |
| CN105469419A (en) * | 2016-01-07 | 2016-04-06 | 于翠妮 | Pulmonary nodule detection method based on CT image |
| CN107590809A (en) * | 2017-06-30 | 2018-01-16 | 上海联影医疗科技有限公司 | Lung dividing method and medical image system |
| CN110619945A (en) * | 2018-06-19 | 2019-12-27 | 西门子医疗有限公司 | Characterization of quantities of training for input to machine learning networks |
| CN109035283A (en) * | 2018-09-05 | 2018-12-18 | 中国医科大学附属盛京医院 | It is a kind of precisely to be detected and quantitative analysis method based on the pulmonary emphysema for randomly selecting subregion |
| CN109658411A (en) * | 2019-01-21 | 2019-04-19 | 杭州英库医疗科技有限公司 | A kind of correlation analysis based on CT images feature Yu Patients with Non-small-cell Lung prognosis situation |
| CN110391015A (en) * | 2019-06-14 | 2019-10-29 | 广东省人民医院(广东省医学科学院) | A method of tumor immunity is quantified based on image group |
| CN110598533A (en) * | 2019-07-31 | 2019-12-20 | 平安科技（深圳）有限公司 | Tongue picture matching method, electronic device, computer device, and storage medium |
| CN111091115A (en) * | 2019-12-31 | 2020-05-01 | 深圳中兴网信科技有限公司 | Vehicle monitoring method and device, computer equipment and storage medium |
| CN111105414A (en) * | 2019-12-31 | 2020-05-05 | 杭州依图医疗技术有限公司 | Processing method, interaction method, display method and storage medium |
| CN111047591A (en) * | 2020-03-13 | 2020-04-21 | 北京深睿博联科技有限责任公司 | Focal volume measuring method, system, terminal and storage medium based on deep learning |
Non-Patent Citations (1)
| Title |
|---|
| 黄祥等.螺旋断层和旋转调强技术在小细胞肺癌脑预防放射治疗海马保护的剂量学研究.《中国医学装备》.2018,第第15卷卷(第第15卷期),第22-26页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111738980A (en) | 2020-10-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109966662B (en) | System for verifying radiation therapy dosage | |
| Gu et al. | Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs | |
| US20210056687A1 (en) | Systems and methods for segmentation of intra-patient medical images | |
| CN110785815B (en) | Parenteral nutrition diagnostic systems, devices and methods | |
| US9218661B2 (en) | Image analysis for specific objects | |
| CN115554619A (en) | Radiation therapy interactive planning | |
| CN107077736A (en) | System and method according to the Image Segmentation Methods Based on Features medical image based on anatomic landmark | |
| EP3212287A1 (en) | Physician directed radiation treatment planning | |
| Sarker et al. | Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm | |
| Fick et al. | Fully automatic brain tumor segmentation for 3D evaluation in augmented reality | |
| Widiasri et al. | Dental-yolo: Alveolar bone and mandibular canal detection on cone beam computed tomography images for dental implant planning | |
| Bispo et al. | Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network | |
| Kano et al. | Automatic contour segmentation of cervical cancer using artificial intelligence | |
| Dai et al. | Locating anatomical landmarks on 2D lateral cephalograms through adversarial encoder-decoder networks | |
| WO2017136762A1 (en) | Methods and systems for enhanced measurement of near heart aorta sections | |
| WO2018219818A1 (en) | Quantified aspects of lesions in medical images | |
| CN117877691B (en) | Intelligent wound information acquisition system based on image recognition | |
| Xu et al. | A 3D segmentation network of mandible from CT scan with combination of multiple convolutional modules and edge supervision in mandibular reconstruction | |
| CN111738980B (en) | Medical image display method, computer equipment and storage medium | |
| CN115274119B (en) | Construction method of immunotherapy prediction model fusing multi-image mathematical characteristics | |
| CN111739645B (en) | Training method of immune-related pneumonia prediction model | |
| Dickson et al. | A Dual Channel Multiscale Convolution U-Net Methodfor Liver Tumor Segmentation from Abdomen CT Images | |
| US11615881B2 (en) | Liver cancer detection | |
| Kwon et al. | Comparison of pre-processed brain tumor MR images using deep learning detection algorithms | |
| Perera-Bel et al. | Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220318 Address after: 100089 area a, 21 / F, block a, No. 8, Haidian Street, Haidian District, Beijing Applicant after: BEIJING SHENRUI BOLIAN TECHNOLOGY Co.,Ltd. Applicant after: SHENZHEN DEEPWISE BOLIAN TECHNOLOGY Co.,Ltd. Address before: 200051 units 06 and 07, 23 / F, 523 Loushanguan Road, Changning District, Shanghai Applicant before: SHANGHAI YIZHI MEDICAL TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |