CN113706560A

CN113706560A - Ischemia area segmentation method, device, equipment and storage medium

Info

Publication number: CN113706560A
Application number: CN202111113172.6A
Authority: CN
Inventors: 李敬伟; 徐运; 张冰; 罗云; 张鑫
Original assignee: Nanjing Drum Tower Hospital
Current assignee: Nanjing Drum Tower Hospital
Priority date: 2021-09-23
Filing date: 2021-09-23
Publication date: 2021-11-26

Abstract

The invention provides a method, a device, equipment and a storage medium for segmenting an ischemic area, wherein the method comprises the following steps: acquiring a cerebral blood flow image of a brain of a target subject; calculating a Z-score map corresponding to the cerebral blood flow image based on a predetermined cerebral image dataset of healthy people; and processing the cerebral blood flow image and the Z-fraction map by using a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target object. The ischemic area segmentation method provided by the invention is used for segmenting the ischemic area based on the statistical characteristics of the cerebral blood flow image, and the accuracy and the robustness of the segmentation of the ischemic area can be improved by comprehensively considering the information of an individual and the information compared with a healthy population.

Description

Ischemia area segmentation method, device, equipment and storage medium

Technical Field

The present invention relates to the field of image processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for segmenting an ischemic area.

Background

Ischemic stroke is a common cerebrovascular disease, seriously harms human health all the time, and has very high morbidity, disability rate and fatality rate. How to diagnose ischemic stroke quickly and effectively is an important research topic in current clinical work. The early detection of the ischemic stroke is very important, especially the correct detection of the hyperacute phase, can guide to take measures in time so as to reduce the brain cell necrosis of the ischemic penumbra area around the infarction focus, and has important effect on the diagnosis and treatment of the ischemic stroke.

The prior art generally uses brain parenchymal imaging for disease diagnosis, efficacy evaluation and prognosis, among which the Arterial Spin Labeling (ASL) perfusion imaging technique is commonly used because the ASL perfusion imaging technique can non-invasively measure the absolute value of the cerebral blood flow. Based on the ASL perfusion data, a Cerebral Blood Flow (CBF) image can be calculated, and based on the CBF image, determination of an ischemic region can be performed. Currently, a common approach to automatic ischemic area segmentation using CBF images is to threshold segmentation based on 40% of the contralateral CBF values, less than 40% being ischemic areas.

However, since the existing CBF image-based ischemia segmentation technology only considers the information of the individual itself, the normal CBF value of some positions of the individual is low, which easily results in segmentation errors, and the judgment method comparing the normal CBF value with the contralateral value may miss the possibility of bilateral ischemia, resulting in poor accuracy of the segmentation result.

Disclosure of Invention

In view of the foregoing problems in the prior art, it is an object of the present invention to provide an ischemic area segmentation method, apparatus, device and storage medium, which can improve the accuracy and robustness of ischemic area segmentation.

In order to solve the above problems, the present invention provides an ischemic area segmentation method including:

acquiring a cerebral blood flow image of a brain of a target subject;

calculating a Z-score map corresponding to the cerebral blood flow image based on a predetermined cerebral image dataset of healthy people;

and processing the cerebral blood flow image and the Z-fraction map by using a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target object.

Further, the acquiring an image of cerebral blood flow of the brain of the target subject includes:

acquiring arterial spin labeling perfusion image data of a brain of a target object;

determining the cerebral blood flow image from the arterial spin-labeled perfusion data.

Further, the acquiring the cerebral blood flow image of the brain of the target subject further comprises:

acquiring a T1 structural image of the target subject brain;

and registering the cerebral blood flow image to a standard brain space based on the T1 structural image to obtain a cerebral blood flow image of the standard brain space.

Specifically, the registering the cerebral blood flow image to a standard brain space based on the T1 structural image, and obtaining the cerebral blood flow image of the standard brain space includes:

acquiring a proton density weighted image of the brain of a target object;

registering the proton density weighted image to the T1 structural image, resulting in a first transformation parameter;

registering the T1 structural image to a standard brain space to obtain a second conversion parameter;

determining a target conversion parameter for converting the arterial spin labeling perfusion data space to a standard brain space according to the first conversion parameter and the second conversion parameter;

and registering the cerebral blood flow image to a standard cerebral space by using the target conversion parameter to obtain the cerebral blood flow image of the standard cerebral space.

Further, the brain image dataset of the healthy population comprises a cerebral blood flow image set of a standard brain space;

the calculating a Z-score map corresponding to the cerebral blood flow image based on a predetermined brain image dataset of a healthy population comprises:

and calculating the Z fraction of each pixel point in the cerebral blood flow image based on the cerebral blood flow image set of the standard cerebral space to obtain the Z fraction image.

Further, the processing the cerebral blood flow image and the Z-fraction map by using a pre-trained classification model to obtain an ischemic zone segmentation image of the brain of the target subject includes:

inputting the cerebral blood flow image and the Z-point map into the classification model to obtain a classification image of the brain of the target object, wherein each pixel in the classification image is an area category identifier;

and fusing the classified image and the artery spin labeling perfusion image data to obtain an ischemic region segmentation image of the brain of the target object.

Further, the arterial spin labeling perfusion image data comprises a proton density weighted image;

the step of fusing the classified image with the artery spin labeling perfusion image data to obtain the ischemic zone segmentation image of the brain of the target object comprises:

converting the classified image into an arterial spin labeling perfusion data space to obtain a classified image of the arterial spin labeling perfusion data space;

and fusing the classified image of the artery spin labeling perfusion data space and the proton density weighted image to obtain an ischemic region segmentation image of the brain of the target object.

Another aspect of the present invention provides an ischemic zone dividing apparatus comprising:

the image acquisition module is used for acquiring a cerebral blood flow image of the brain of the target object;

the system comprises a first calculation module, a second calculation module and a third calculation module, wherein the first calculation module is used for calculating a Z-fraction map corresponding to a cerebral blood flow image based on a predetermined cerebral image data set of healthy people;

and the image processing module is used for processing the cerebral blood flow image and the Z-fraction map by utilizing a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target object.

Another aspect of the present invention provides an electronic device, including a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to implement the ischemic zone segmentation method as described above.

Another aspect of the present invention provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the ischemic zone segmentation method as described above.

Due to the technical scheme, the invention has the following beneficial effects:

according to the ischemic region segmentation method provided by the embodiment of the invention, the ischemic region segmentation image is obtained by calculating the Z-score map corresponding to the cerebral blood flow image of the target brain based on the predetermined brain image dataset of healthy people, and then performing machine learning classification based on the cerebral blood flow image and the Z-score map. The method for segmenting the ischemic region based on the statistical characteristics of the cerebral blood flow image comprehensively considers the information of an individual and the information compared with a healthy group, and can improve the accuracy and the robustness of segmenting the ischemic region.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiment or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

FIG. 1 is a schematic illustration of an implementation environment provided by an embodiment of the invention;

fig. 2 is a flowchart of an ischemic area segmentation method according to an embodiment of the present invention;

FIG. 3 is a schematic illustration of registration to standard brain space provided by an embodiment of the present invention;

FIG. 4 is a graph illustrating the results of ischemic zone segmentation provided by one embodiment of the present invention;

fig. 5 is a schematic structural diagram of an ischemic area segmentation apparatus according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or device.

Referring to the specification, fig. 1 is a schematic diagram illustrating an implementation environment provided by an embodiment of the invention. As shown in fig. 1, the implementation environment may include at least one medical scanning device 110 and a computer device 120, where the computer device 120 and each medical scanning device 110 may be directly or indirectly connected through wired or wireless communication, and the embodiment of the present invention is not limited thereto.

The computer device 120 may acquire medical image data of the brain of the target subject scanned by the medical scanning device 110, and determine an ischemic area segmentation image of the brain of the target subject by using the ischemic area segmentation method provided by the embodiment of the present invention, so as to be referred by a doctor and guide the doctor to take measures in time. The medical scanning device 110 may be but not limited to a magnetic resonance imaging device, and the like, the computer device 120 may be but not limited to various servers, personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, the server may be an independent server or a server cluster or a distributed system composed of a plurality of servers, and may also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, Network services, cloud communications, middleware services, domain name services, security services, Content Delivery Networks (CDNs), and big data and artificial intelligence platforms.

It should be noted that fig. 1 is only an example. It will be appreciated by those skilled in the art that although only 1 medical scanning device 110 is shown in FIG. 1, it is not intended to limit embodiments of the present invention and that more or fewer medical scanning devices 110 may be included than shown.

Referring to the specification fig. 2, which illustrates a flowchart of an ischemic zone segmentation method according to an embodiment of the present invention, the method may be applied to the computer device 120 in fig. 1, and specifically, as shown in fig. 2, the method may include the following steps:

s210: an image of cerebral blood flow of the brain of the target subject is acquired.

In the embodiment of the invention, the ASL perfusion imaging technology can be utilized to collect the image of the brain of the target object to obtain the corresponding ASL perfusion image data, and then the CBF image is obtained by utilizing the ASL perfusion image data for calculation. Wherein the target object may be a patient who may have a brain disease.

Specifically, the acquiring of the cerebral blood flow image of the brain of the target subject may include:

In an embodiment of the invention, the ASL perfusion image data may comprise a proton density weighted image (M0 image), a control image (i.e. a non-label image) and a label image (label image), and the CBF image may be calculated by controlling the control image and the label image. Specifically, for an image acquired with a pulse-labeled ASL sequence, the CBF calculation formula for each voxel is:

for images acquired using a continuous or pseudo-continuous labeled ASL sequence, the CBF calculation formula for each voxel is:

wherein, the lambda is brain tissue/blood flow distribution coefficient, the size is 0.9ml/g, and the SI is_controlAnd SI_labelTime-averaged signal intensity, T, for control and label images, respectively_1,bloodThe longitudinal relaxation time of blood is expressed in seconds, α is the labeling efficiency, α is 0.85 for continuous or pseudo-continuous labeling ASL sequences, and α is 0.98 for pulsed labeling ASL sequences. SI (Standard interface)_PDIs the signal intensity of the proton density weighted image, and τ is the mark duration. The PLD is the post-marker delay time, i.e. how long after the marker data starts to be acquired, TI is the inversion time, note that TI is the ASL sequence term for pulse-type markers, and PLD is the ASL sequence term for pseudo-continuous markers.

TI₁The labeled blood flow in the ASL sequence, which is pulse-labeled, reaches the temporal width of the scan field. In the formulae (1) and (2), the values of λ and α are known, SI_control、SI_label、SI_PDRequired to be collected fromIn the image, T is obtained_1,bloodτ, PLD, TI and TI₁Are obtained from the ASL sequence information of the magnetic resonance system.

It should be noted that, the above method for calculating a CBF image by using ASL perfusion image data may be executed by a computer device implementing the method provided by the embodiment of the present invention, or may be executed by another device, and the obtained CBF image is sent to the computer device, which is not limited by the embodiment of the present invention.

It should be noted that, in some possible embodiments, images of the brain of the target object may also be acquired by other imaging technologies in the prior art (for example, a nuclear magnetic perfusion imaging technology, a CT perfusion imaging technology, and the like), and a corresponding cerebral blood flow image is calculated, which is not limited in this embodiment of the present invention.

In one possible embodiment, after the CBF image is acquired, it may be registered to standard brain space to simplify subsequent processing. Specifically, the acquiring the cerebral blood flow image of the brain of the target subject may further include:

acquiring a T1 structural image of the target subject brain;

In the embodiment of the present invention, the target brain may be scanned again to obtain the T1 structural image. Since the CBF image is a parametric map, which has a large difference from the structural map, and if the CBF image is directly registered to the standard brain space, the error is large, so the original image is generally used for registration (for example, using the M0 image in the ASL perfusion image data) to obtain the conversion parameter from the ASL perfusion data space to the standard brain space, and then the CBF image is converted to the standard brain space according to the obtained conversion parameter to obtain the CBF image of the standard brain space. This is because the CBF images are computed from the ASL perfusion data and therefore their spaces are consistent (i.e., the same pixels are located at the same location), so that after the M0 images are registered to the standard brain space, the CBF images can be transformed to the standard brain space using the same transformation parameters.

Specifically, the registering the cerebral blood flow image to a standard brain space based on the T1 structural image, and obtaining the cerebral blood flow image of the standard brain space may include:

acquiring a proton density weighted image of the brain of a target object;

In practical application, referring to fig. 3 of the specification, an M0 image in ASL perfusion image data may be registered to a T1 structural image to obtain a first transformation parameter, then the T1 structural image may be registered to an MNI image in a standard brain space to obtain a second transformation parameter for transforming the T1 structural image to the standard brain space, and finally a target transformation parameter for transforming the ASL perfusion data space to the standard brain space may be determined based on the first transformation parameter and the second transformation parameter.

S220: and calculating a Z-fraction map corresponding to the cerebral blood flow image based on a predetermined cerebral image dataset of the healthy population.

In the embodiment of the invention, the statistical characteristics of each pixel point in the CBF image, namely the difference (namely Z score) between the CBF value of each pixel point in the CBF image and the average value of the healthy population, can be calculated based on the brain image data set of the healthy population, and the ischemic region of the target brain is segmented by using the calculated statistical characteristic image (namely Z score image).

In particular, the brain image dataset of the healthy population may comprise a cerebral blood flow image set of a standard brain space.

In practical application, ASL perfusion data of a preset number of healthy people can be collected in advance, corresponding CBF images can be calculated for the ASL perfusion data of each healthy people, the calculated CBF images are registered to a standard brain space, CBF images of the standard brain space are obtained, and therefore a CBF image set of the standard brain space of the healthy people can be established finally. The ASL perfusion data of the healthy population can be image data with high quality and without motion artifacts, so that the CBF image calculated based on the ASL perfusion data has no artifacts.

It should be noted that the preset number may be set according to actual needs, for example, may be set to be at least 500, which is not limited in this embodiment of the present invention, and the specific calculation method of the CBF images of the healthy people and the specific method of registering the CBF images in the standard brain space may refer to the specific content in step S210, which is not described herein again in the embodiment of the present invention.

In some possible embodiments, other imaging technologies (e.g., a nuclear magnetic perfusion imaging technology, a CT perfusion imaging technology, etc.) may also be used to acquire images of brains of a preset number of healthy people, and calculate to obtain corresponding CBF images, and then register the CBF images in the standard brain space to obtain CBF images in the standard brain space, so that a CBF image set in the standard brain space of the healthy people may be finally established.

It should be noted that, when the CBF image of the target brain and the CBF image of the brain of the healthy population are acquired and calculated by using the same imaging technology, the Z-score map corresponding to the CBF image of the target brain can be directly calculated based on the CBF image set of the standard brain space of the healthy population. When different imaging technologies are adopted to acquire and calculate the CBF image of the target object brain and the CBF image of the healthy crowd brain, normalization processing can be performed on the CBF image of the target object brain and the CBF image in the CBF image set of the healthy crowd, and a Z-score map corresponding to the CBF image of the target object brain is calculated based on the image data after the normalization processing.

Specifically, the calculating a Z-score map corresponding to the cerebral blood flow image based on a predetermined brain image dataset of a healthy population may include:

In the embodiment of the invention, for each pixel point in the CBF image of the standard brain space, the difference (namely Z fraction) between the CBF value of the pixel point and the average value of healthy people can be calculated by adopting the following formula:

wherein CBF is the value of the current pixel point of the CBF image, F_iThe value of a pixel point corresponding to the ith CBF image in the cerebral blood flow image set of the standard cerebral space of the healthy crowd is n, and the number of the CBF images in the cerebral blood flow image set of the standard cerebral space of the healthy crowd is n. Thus, a Z-score map corresponding to the CBF image of the target brain is generated, and the value of each pixel point in the map represents the Z score of the corresponding pixel point of the CBF image of the target brain in healthy people.

S230: and processing the cerebral blood flow image and the Z-fraction map by using a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target object.

In the embodiment of the invention, the cerebral blood flow image and the Z-fraction image can be combined to automatically segment the ischemic area by using a machine learning algorithm. Wherein the ischemic region segmentation image may include at least one of an ischemic region and a normal region.

Specifically, the processing the cerebral blood flow image and the Z-fraction map by using a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target subject may include:

In the embodiment of the invention, the classification model can analyze and process the input cerebral blood flow image and the Z-score image, determine the region type of each pixel point of the target object brain image, and distinguish the pixel points through different region type identifications. The classification model may be obtained by training a preset neural network model through training image data labeled with a region class in advance, where the preset neural network model may include, but is not limited to, a classification model commonly used in the prior art, and may be, for example, a Random Forest (RF) model or a Support Vector Machine (SVM) model, and the like. The region category may include an ischemic region and a normal region, and different identifiers may be used to distinguish different regions, for example, 1 may be used to identify the ischemic region, 0 may be used to identify the normal region, and so on. It should be noted that the above distinguishing method is not limited to the embodiment of the present invention, and other identification methods may be used to distinguish different area types.

In the embodiment of the invention, in combination with the reference specification and the attached fig. 4, in order to more intuitively display each region, the classification image and the image of the ASL perfusion data space may be fused to obtain the ischemia region segmentation image, and the obtained ischemia region may be marked, so that a doctor may quickly determine information such as the position and the range of the ischemia region. Illustratively, as shown in fig. 4, the area within the frame is an ischemic area, and the other areas are normal areas.

In one possible embodiment, the arterial spin labeling perfusion image data may include a proton density weighted image; the fusing the classified image with the arterial spin labeling perfusion image data to obtain the segmentation image of the ischemic region of the brain of the target subject may include:

In practical applications, the classification image may be fused with an original image (e.g., a proton density weighted image) to obtain the ischemia region segmentation image. Specifically, a corresponding Z-score map is calculated by using a cerebral blood flow image of a standard brain space, the cerebral blood flow image of the standard brain space and the corresponding Z-score map are input into the classification model, a classification image of the standard brain space can be obtained, at this time, the classification image may be converted into an ASL perfusion data space to obtain a classification image of the ASL perfusion data space, and then the classification image of the ASL perfusion data space and the proton density weighted image are superimposed to obtain an ischemia region segmentation image of the ASL perfusion data space. The classified images can be converted into the ASL perfusion data space by performing inverse operation by using the target conversion parameter which is determined when the CBF images are registered to the standard brain space and is converted from the ASL perfusion data space to the standard brain space.

In practical applications, the ischemic area in the classification image of the ASL perfusion data space may be labeled with a color and superimposed on the proton density weighted image, for example, the ischemic area may be labeled with a green color, and the color may be used to label the ischemic area, so that a doctor may quickly and accurately determine information such as the location and the range of the ischemic area.

In one possible embodiment, the method may further comprise the step of training the classification model using training image data of pre-labeled region classes.

Specifically, a training image data set may be obtained in advance, each training image in the training image data set may be labeled pixel by pixel, the regions labeled as the classification are a normal region and an ischemic region, and then a preset neural network model may be trained by using the labeled training image data set to obtain the classification model. The preset neural network model may include, but is not limited to, a classification model commonly used in the prior art, for example, an RF model or an SVM, and the embodiment of the present invention is not limited thereto.

Specifically, in the process of training the classification model, a verification image data set may be further obtained, where the verification image data set is used to evaluate the performance of the classification model, that is, to test the performance of the trained model, and when the trained classification model meets a preset condition, the training is completed to obtain the classification model that can be used for classification.

In practical application, the corresponding CBF image and the corresponding Z-score image thereof may be determined according to the verification image dataset, the trained classification model is verified by using the two feature images, and when the trained classification model satisfies a preset condition, the classification model that may be used for classification is obtained. The preset condition may be preset, for example, the correlation between the DICE coefficient or the volume value between the segmented ischemic zone and the actual ischemic zone may be set to be greater than a preset value, and the preset value may be set according to an actual situation, which is not limited in this embodiment of the present invention.

In one possible embodiment, the method may further include the step of calculating an ischemic area volume from the ischemic area segmentation image.

In the embodiment of the invention, after the segmentation image of the ischemic area is obtained, the number of the pixel points of the ischemic area can be determined, and then the volume of each pixel point is multiplied to obtain the volume of the segmented ischemic area. The ischemic area of the ischemic stroke of the target user can be effectively evaluated by calculating the volume of the ischemic area, and the method has guiding significance for diagnosis and treatment of the ischemic stroke.

In summary, according to the ischemic region segmentation method of the embodiment of the present invention, the ischemic region segmentation image is obtained by first calculating the Z-score map corresponding to the cerebral blood flow image of the target brain based on the predetermined brain image dataset of the healthy population, and then performing machine learning classification based on the cerebral blood flow image and the Z-score map. The method for segmenting the ischemic region based on the statistical characteristics of the cerebral blood flow image comprehensively considers the information of an individual and the information compared with a healthy group, and can improve the accuracy and the robustness of segmenting the ischemic region.

Referring to the specification and fig. 5, there is shown a structure of an ischemic area dividing apparatus 500 according to an embodiment of the present invention. As shown in fig. 5, the apparatus 500 may include:

an image acquisition module 510 for acquiring a cerebral blood flow image of the brain of the target subject;

a first calculating module 520, configured to calculate a Z-score map corresponding to the cerebral blood flow image based on a predetermined cerebral image dataset of a healthy population;

an image processing module 530, configured to process the cerebral blood flow image and the Z-fraction map by using a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target object.

In one possible embodiment, the apparatus 500 may further include a second calculation module for calculating the ischemic area volume according to the ischemic area segmentation image.

It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus provided in the above embodiments and the corresponding method embodiments belong to the same concept, and specific implementation processes thereof are detailed in the corresponding method embodiments and are not described herein again.

An embodiment of the present invention further provides an electronic device, which includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the ischemic zone segmentation method provided in the above method embodiment.

The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.

In a specific embodiment, fig. 6 is a schematic diagram illustrating a hardware structure of an electronic device for implementing the ischemic area segmentation method provided by the embodiment of the present invention, where the electronic device may be a computer terminal, a mobile terminal, or another device, and the electronic device may also participate in forming or including the ischemic area segmentation apparatus provided by the embodiment of the present invention. As shown in fig. 6, the electronic device 600 may include one or more computer-readable storage media of the memory 610, one or more processing cores of the processor 620, an input unit 630, a display unit 640, a Radio Frequency (RF) circuit 650, a wireless fidelity (WiFi) module 660, and a power supply 670. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 6 does not constitute a limitation of electronic device 600, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components. Wherein:

the memory 610 may be used to store software programs and modules, and the processor 620 may execute various functional applications and data processing by operating or executing the software programs and modules stored in the memory 610 and calling data stored in the memory 610. The memory 610 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to use of the electronic device, and the like. In addition, the memory 610 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device. Accordingly, the memory 610 may also include a memory controller to provide the processor 620 with access to the memory 610.

The processor 620 is a control center of the electronic device 600, connects various parts of the whole electronic device by using various interfaces and lines, and performs various functions of the electronic device 600 and processes data by operating or executing software programs and/or modules stored in the memory 610 and calling data stored in the memory 610, thereby performing overall monitoring of the electronic device 600. The Processor 620 may be a central processing unit, or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The input unit 630 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 630 may include a touch sensitive surface 631 as well as other input devices 632. In particular, the touch-sensitive surface 631 may include, but is not limited to, a touch pad or touch screen, and the other input devices 632 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

The display unit 640 may be used to display information input by or provided to a user and various graphical user interfaces of an electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. The Display unit 640 may include a Display panel 641, and optionally, the Display panel 641 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

The RF circuit 650 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, for receiving downlink information of a base station and then processing the received downlink information by the one or more processors 620; in addition, data relating to uplink is transmitted to the base station. In general, RF circuit 650 includes, but is not limited to, an antenna, at least one Amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, RF circuit 650 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), and the like.

WiFi belongs to short-distance wireless transmission technology, and the electronic equipment 600 can help a user to send and receive e-mails, browse webpages, access streaming media and the like through the WiFi module 660, and provides wireless broadband Internet access for the user. Although fig. 6 shows the WiFi module 660, it is understood that it does not belong to the essential constitution of the electronic device 600, and may be omitted entirely as needed within the scope not changing the essence of the invention.

The electronic device 600 also includes a power supply 670 (e.g., a battery) for powering the various components, which may be logically coupled to the processor 620 via a power management system to manage charging, discharging, and power consumption management functions via the power management system. The power supply 670 may also include one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, or any other component.

It should be noted that, although not shown, the electronic device 600 may further include a bluetooth module, and the like, which is not described herein again.

An embodiment of the present invention further provides a computer-readable storage medium, which may be disposed in an electronic device to store at least one instruction or at least one program for implementing an ischemic zone segmentation method, where the at least one instruction or the at least one program is loaded and executed by the processor to implement the ischemic zone segmentation method provided by the above-mentioned method embodiment.

Optionally, in an embodiment of the present invention, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

An embodiment of the invention also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the ischemic area segmentation method provided in the various alternative embodiments described above.

It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A method of ischemic area segmentation, comprising:

acquiring a cerebral blood flow image of a brain of a target subject;

2. The method of claim 1, wherein the obtaining an image of cerebral blood flow in the brain of the target subject comprises:

3. The method of claim 2, wherein the obtaining an image of cerebral blood flow to the brain of the target subject further comprises:

acquiring a T1 structural image of the target subject brain;

4. The method of claim 3, wherein registering the cerebral blood flow image to a standard cerebral space based on the T1 structure image, resulting in a cerebral blood flow image of a standard cerebral space comprises:

acquiring a proton density weighted image of the brain of a target object;

5. The method of claim 1, wherein the healthy population brain image dataset comprises a standard brain space cerebral blood flow image set;

6. The method of claim 1, wherein the processing the cerebral blood flow image and the Z-score map using a pre-trained classification model to obtain an ischemic zone segmentation image of the brain of the target subject comprises:

7. The method of claim 6, wherein the arterial spin labeling perfusion image data comprises a proton density weighted image;

8. An ischemic area dividing device comprising:

9. An electronic device, comprising a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to implement the ischemic area segmentation method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the computer-readable storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the ischemic area dividing method according to any one of claims 1-7.