Method and device for training segmentation model for segmenting aneurysm and storage medium

Abstract

The invention discloses a method and equipment for training a segmentation model for segmenting an aneurysm, and a storage medium. The method comprises the following steps: acquiring multi-modal image data, wherein the multi-modal image data comprises digital subtraction angiography data, CT angiography data and magnetic resonance angiography data; based on the multi-modal image data, performing blood vessel segmentation and labeling a target aneurysm to obtain a blood vessel segmentation result and a target aneurysm labeling result; constructing a distance map and label data according to the blood vessel segmentation result and the target aneurysm labeling result; and inputting the blood vessel segmentation result, the distance map and the label data into a segmentation model for performing aneurysm segmentation to obtain a target aneurysm, so as to train the segmentation model for segmenting the aneurysm. By using the scheme of the invention, the segmentation precision of the segmentation model can be improved so as to obtain a more accurate target aneurysm.

Description

Technical Field

[0001] The present application generally relates to the technical field of aneurysm segmentation. More specifically, the present application relates to a method for training a segmentation model for segmenting an aneurysm, a method, a device and a computer-readable storage medium for segmenting an aneurysm. Background Art

[0002] Aneurysm is a type of lesion in the blood vessels, which refers to the thickening of the artery diameter and the manifestation of tumor-like expansion. In physical examination screening, imaging examination of the blood vessels is often used to screen for aneurysms. The aforementioned images include digital subtraction angiography ("DSA"), CT vascular imaging ("CTA"), or magnetic resonance angiography ("MRA"). At present, for the segmentation of aneurysms, aneurysms are directly annotated in the image data, and the annotated images are input into the segmentation model for aneurysm segmentation. However, there are differences in the size of aneurysms in different imaging examinations, and direct annotation based on image data cannot balance the characteristics of aneurysms of different sizes. In addition, due to the presence of multiple aneurysms close to each other, especially for small aneurysms close to large arteries, the existing segmentation often only segments large aneurysms and ignores small arteries, thereby affecting the segmentation accuracy of aneurysms.

[0003] In view of this, there is an urgent need to provide a solution for training a segmentation model for segmenting aneurysms, so as to improve the segmentation accuracy of the segmentation model and obtain a more accurate aneurysm segmentation result. Summary of the invention

[0004] In order to at least solve one or more of the technical problems mentioned above, the present application proposes a solution for training a segmentation model for segmenting aneurysms in multiple aspects.

[0005] In a first aspect, the present application provides a method for training a segmentation model for segmenting an aneurysm, comprising: acquiring multimodal image data, wherein the multimodal image data comprises digital subtraction angiography data, CT vascular imaging data and magnetic resonance vascular imaging data; performing vascular segmentation and labeling a target aneurysm based on the multimodal image data to obtain a vascular segmentation result and a target aneurysm labeling result; constructing a distance map and label data according to the vascular segmentation result and the target aneurysm labeling result; and inputting the vascular segmentation result, the distance map and the label data into a segmentation model to perform aneurysm segmentation to obtain a target aneurysm, so as to train the segmentation model for segmenting an aneurysm.

[0006] In some embodiments, the distance map is constructed by the following operations: arbitrarily selecting a positioning point in the target aneurysm annotation result; and calculating the shortest path from each pixel point in the foreground area in the blood vessel segmentation result to the positioning point to construct the distance map, wherein the shortest path does not pass through the background area in the blood vessel segmentation result.

[0007] In some other embodiments, the label data is constructed by the following operations: setting the corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm labeling result as the first label data; and setting the corresponding pixel points located in the target aneurysm labeling result where the positioning point is located as the second label data.

[0008] In some other embodiments, the blood vessel segmentation result, the distance map and the label data are input into a segmentation model to perform aneurysm segmentation to obtain a target aneurysm, so as to train the segmentation model for segmenting aneurysms, including: inputting the blood vessel segmentation result, the distance map, the first label data and the second label data into the segmentation model to perform aneurysm segmentation, and outputting different categories to obtain a target aneurysm, so as to train the segmentation model for segmenting aneurysms.

[0009] In some other embodiments, the label data is constructed by the following operations: setting corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm labeling result as first label data; setting corresponding pixel points located in the target aneurysm labeling result that does not contain the positioning point as second label data; and setting corresponding pixel points located in the target aneurysm labeling result where the positioning point is located as third label data.

[0010] In some other embodiments, inputting the blood vessel segmentation result, the distance map and the label data into a segmentation model to perform aneurysm segmentation to obtain a target aneurysm for training the segmentation model for segmenting aneurysms also includes: inputting the blood vessel segmentation result, the distance map, the first label data, the second label data and the third label data into the segmentation model to perform aneurysm segmentation, and outputting different categories to obtain a target aneurysm for training the segmentation model for segmenting aneurysms.

[0011] In some other embodiments, the segmentation model includes a Unet model, a FPN model or a PSPNet model.

[0012] In a second aspect, the present application provides a method for segmenting an aneurysm, comprising: acquiring image data to be segmented and extracting a binary image and a distance image of blood vessel segmentation based on the image data to be segmented; and inputting the binary image and the distance image of blood vessel segmentation into a segmentation model trained by one or more embodiments of the first aspect to perform aneurysm segmentation to obtain a target aneurysm.

[0013] In a third aspect, the present application provides a device, comprising: a processor; and a memory, on which computer instructions for training a segmentation model for segmenting an aneurysm are stored, and when the computer instructions are executed by the processor, multiple embodiments of the first aspect are implemented; or computer instructions for segmenting an aneurysm are stored, and when the computer instructions are executed by the processor, one embodiment of the second aspect is implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing computer program instructions for training a segmentation model for segmenting an aneurysm, which, when executed by one or more processors, implements multiple embodiments of the first aspect; or storing computer instructions for segmenting an aneurysm, which, when executed by a processor, implements one embodiment of the second aspect.

[0015] Through the scheme for training the segmentation model for segmenting aneurysms as provided above, the embodiment of the present application uses multimodal image data, and performs vascular segmentation and aneurysm annotation on the multimodal image data to obtain vascular segmentation results and aneurysm annotation results, and at the same time combines the vascular segmentation results and aneurysm annotation results of different image data to construct label data. Among them, the vascular segmentation results are consistent in different modalities, so the embodiment of the present application can more accurately annotate aneurysms through the morphological characteristics of the blood vessels, thereby improving the segmentation accuracy of the segmentation model. In addition, the embodiment of the present application adds a distance feature (i.e., a distance map), which can better distinguish the target aneurysm from other aneurysms to improve the segmentation accuracy of small aneurysms. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easy to understand. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0017] Figure 1 is an exemplary flowchart showing a method for training a segmentation model for segmenting an aneurysm according to an embodiment of the present application;

[0018] Figure 2is an exemplary flowchart showing the overall process of training a segmentation model for segmenting an aneurysm according to an embodiment of the present application;

[0019] Figure 3 is another exemplary flowchart showing the overall training of a segmentation model for segmenting an aneurysm according to an embodiment of the present application;

[0020] Figure 4 is an exemplary flowchart showing a method for segmenting an aneurysm according to an embodiment of the present application;

[0021] Figure 5 is a block diagram showing an exemplary structure of a device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0023] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present application indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this application specification and claims, unless the context clearly indicates otherwise, the singular forms of "a", "an" and "the" are intended to include plural forms. It should also be further understood that the term "and / or" used in this application specification and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0025] As used in this specification and claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0026] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0027] Figure 1 FIG. 1 is an exemplary flowchart showing a method 100 for training a segmentation model for segmenting an aneurysm according to an embodiment of the present application. Figure 1 As shown in , the method 100 includes: step S101: acquiring multimodal image data, wherein the multimodal image data includes digital subtraction angiography data, CT vascular imaging data and magnetic resonance angiography data; step S102: performing blood vessel segmentation and marking target aneurysms based on the multimodal image data to obtain blood vessel segmentation results and target aneurysm marking results; step S103: constructing a distance map and label data according to the blood vessel segmentation results and the target aneurysm marking results; and step S104: inputting the blood vessel segmentation results, the distance map and the label data into a segmentation model to perform aneurysm segmentation to obtain the target aneurysm, so as to train the segmentation model for segmenting aneurysms.

[0028] First, at step S101, multimodal image data is collected. The multimodal image data may include digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data. Among them, digital subtraction angiography data is the gold standard for screening and diagnosis of various vascular diseases, and is also the gold standard for diagnosis and measurement of aneurysms, while CT angiography data and magnetic resonance angiography data can be used for screening and follow-up of aneurysms. The embodiment of the present application can greatly improve the performance of the segmentation model by using the three modal data for training the segmentation model.

[0029] Next, at step S102, blood vessel segmentation is performed based on the multimodal imaging data and the target aneurysm is labeled to obtain a blood vessel segmentation result and a target aneurysm labeling result. In some embodiments, image processing methods or deep learning methods may be used to perform blood vessel segmentation, wherein the aforementioned image processing methods may include but are not limited to image edge detection and image morphology methods. The aforementioned deep learning method may be based on an existing trained segmentation model to perform blood vessel segmentation to obtain a blood vessel segmentation result, and the present application does not impose any restrictions on this, and the aforementioned blood vessel segmentation result is a binary image. In some implementation scenarios, the aforementioned aneurysm labeling may be performed manually or automatically to label the target aneurysm area in the blood vessel segmentation result to obtain a target aneurysm labeling result.

[0030] Based on the above-obtained vascular segmentation results and target aneurysm labeling results, at step S103, a distance map and label data are constructed according to the vascular segmentation results and the target aneurysm labeling results. Among them, the vascular segmentation results are consistent in different modal data, which is more conducive to aneurysm morphology analysis, thereby more accurately labeling aneurysms. Therefore, combining the vascular segmentation results and the target aneurysm labeling results to construct label data can improve the accuracy of target aneurysm labeling, so as to improve the accuracy of subsequent segmentation model training.

[0031] In some embodiments, the distance map can be constructed by the following operations: arbitrarily selecting a positioning point in the target aneurysm annotation result, calculating the shortest path from each pixel point in the foreground area of ​​the blood vessel segmentation result to the positioning point, so as to construct a distance map, wherein the shortest path does not pass through the background area in the blood vessel segmentation result. Specifically, a positioning point is randomly selected on the target aneurysm annotation result, and for each point in the foreground area of ​​the blood vessel segmentation result, the shortest path d to the positioning point is calculated. The shortest path d does not pass through the background area of ​​the blood vessel segmentation result, that is, for each point in the background area, the corresponding path d=0. Based on this, a distance map can be extracted to more accurately distinguish different aneurysms, and ensure that small aneurysms are segmented, thereby improving the segmentation accuracy.

[0032] In some embodiments, label data can be constructed by the following operations: setting the corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm labeling result as the first label data and setting the corresponding pixel points located in the target aneurysm labeling result where the positioning point is located as the second label data. That is, the blood vessel pixel labeling is set as the first label data, for example, the label can be set to 1. The pixel points located in the target aneurysm labeling result and the positioning point is also in the target aneurysm labeling result are set to the second label data, for example, the label can be set to 2. Among them, label 1 corresponds to non-aneurysm, and label 2 corresponds to target aneurysm.

[0033] Further, at step S104, the blood vessel segmentation result, the distance map and the label data are input into the segmentation model to perform aneurysm segmentation to obtain the target aneurysm, so as to train the segmentation model for segmenting the aneurysm. In some embodiments, the blood vessel segmentation result, the distance map, the first label data and the second label data can be input into the segmentation model for aneurysm segmentation, and different categories can be output to obtain the target aneurysm, so as to train the segmentation model for segmenting the aneurysm. That is, the blood vessel segmentation result and the distance map are used as input data, and the first and second label data are used as labels to train the segmentation model for segmenting the aneurysm. In some implementation scenarios, the aforementioned segmentation model can be, for example, a Unet model, an FPN model or a PSPNet model.

[0034] In combination with the above description, it can be seen that the embodiment of the present application performs vascular segmentation and annotates aneurysms on digital subtraction angiography data, CT vascular imaging data, and magnetic resonance vascular imaging data to obtain vascular segmentation results and aneurysm annotation results, and at the same time trains the segmentation model by combining the label data constructed by the vascular segmentation results and the aneurysm annotation results. Based on this, the problem that the image features cannot be aligned (that is, lack of consistency) when the aneurysm is directly segmented from the image can be avoided, thereby improving the accuracy of aneurysm identification. In addition, the embodiment of the present application also adds a distance feature, which can better distinguish the target aneurysm from other aneurysms to improve the segmentation accuracy of small aneurysms. Furthermore, the embodiment of the present application can more accurately distinguish large aneurysms from small aneurysms by calculating the shortest distance between the positioning point and the vascular foreground pixel, thereby improving the recognition accuracy of the target aneurysm.

[0035] Figure 2 FIG. 1 is an exemplary flowchart showing the overall training of a segmentation model for segmenting an aneurysm according to an embodiment of the present application. It should be understood that Figure 2 is the above Figure 1 A specific implementation of method 100, so the above Figure 1 The description also applies to Figure 2 .

[0036] like Figure 2 As shown in , at step S201, digital subtraction angiography data, CT angiography data and magnetic resonance angiography data are collected. Then, at step S202, blood vessel segmentation is performed based on the aforementioned three multimodal image data to obtain a blood vessel segmentation result. In some embodiments, blood vessel segmentation can be performed by, for example, edge detection, morphology or an existing segmentation model to obtain a blood vessel segmentation result. At step S203, a target aneurysm is annotated in the blood vessel segmentation result to obtain a target aneurysm annotated result.

[0037] Further, at step S204, a positioning point is arbitrarily selected in the target aneurysm annotation result, and at step S205, the shortest path from each pixel point in the foreground area of ​​the blood vessel segmentation result to the positioning point is calculated to obtain a distance map, wherein the shortest path does not pass through the background area of ​​the blood vessel segmentation result.

[0038] The figure further shows that at step S206, the corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm labeling result are set as the first label data, and at step S207, the corresponding pixel points located in the target aneurysm labeling result where the positioning point is located are set as the second label data. Based on the obtained blood vessel segmentation result, the distance map, the first and second label data, at step S208, the blood vessel segmentation result, the distance map, the first label data and the second label data are input into the segmentation model for training to obtain the target aneurysm, so as to obtain a trained segmentation model.

[0039] In some embodiments, the embodiments of the present application can also construct label data by the following operations: setting the corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm labeling result as the first label data, setting the corresponding pixel points located in the target aneurysm labeling result not containing the positioning point as the second label data, and setting the corresponding pixel points located in the target aneurysm labeling result where the positioning point is located as the third label data. That is, the blood vessel pixel labeling is set to the first label data, for example, the label can be set to 1. The pixel points located in the target aneurysm labeling result but not containing the positioning point are set to the second label data, for example, the label can be set to 2. The pixel points located in the target aneurysm labeling result and the positioning point are also in the target aneurysm labeling result are set to the third label data, for example, the label can be set to 3. Among them, label 1 corresponds to non-aneurysm, label 2 corresponds to other aneurysms, and label 3 corresponds to target aneurysm. Based on this, the target aneurysm and other aneurysms can be subdivided to further improve the segmentation accuracy.

[0040] In some embodiments, the blood vessel segmentation result, the distance map, the first label data, the second label data, and the third label data may be input into a segmentation model for aneurysm segmentation, and different categories may be output to obtain a target aneurysm, so as to train the segmentation model for aneurysm segmentation. That is, the first, second, and third label data further constructed are used as labels, and the blood vessel segmentation result and the distance map are used as input data to train the segmentation model.

[0041] Figure 3 is another exemplary flowchart showing the overall training of a segmentation model for segmenting an aneurysm according to an embodiment of the present application. It should be understood that Figure 3 is the above Figure 1 Another specific implementation of the method 100, so the above Figure 1 The description also applies to Figure 3 .

[0042] like Figure 3As shown in , at step S301, digital subtraction angiography data, CT angiography data and magnetic resonance angiography data are collected. Then, at step S302, blood vessel segmentation is performed based on the aforementioned three multimodal image data to obtain a blood vessel segmentation result. In some embodiments, blood vessel segmentation can be performed by, for example, edge detection, morphology or an existing segmentation model to obtain a blood vessel segmentation result. At step S303, a target aneurysm is annotated in the blood vessel segmentation result to obtain a target aneurysm annotation result.

[0043] Further, at step S304, a positioning point is arbitrarily selected in the target aneurysm labeling result, and at step S305, the shortest path from each pixel point in the foreground area of ​​the blood vessel segmentation result to the positioning point is calculated to obtain a distance map. Then, at steps S306, S307, and S308, first label data, second label data, and third label data are constructed, respectively. That is, the corresponding pixel points in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm labeling result are set as the first label data, the corresponding pixel points located in the target aneurysm labeling result not containing the positioning point are set as the second label data, and the corresponding pixel points located in the target aneurysm labeling result where the positioning point is located are set as the third label data.

[0044] After obtaining the aforementioned blood vessel segmentation results, distance map and label data, at step S309, the blood vessel segmentation results, distance map, first label data, second label data and third label data are input into the segmentation model for training to obtain the target aneurysm, so as to obtain a trained segmentation model.

[0045] Figure 4 FIG. 4 is an exemplary flowchart showing a method 400 for segmenting an aneurysm according to an embodiment of the present application. Figure 4 As shown in , the method 400 includes: step S401: acquiring image data to be segmented and extracting a binary image and a distance image of blood vessel segmentation based on the image data to be segmented; step S402: inputting the binary image and the distance image of blood vessel segmentation into a trained segmentation model to perform aneurysm segmentation to obtain a target aneurysm.

[0046] In step S401, the image data to be segmented is obtained and a binary image and a distance image of the blood vessel segmentation are extracted based on the image data to be segmented. As can be seen from the foregoing, the binary image of the blood vessel segmentation can be extracted by methods such as image edge detection, image morphology, or deep learning. In some embodiments, the distance image can be obtained by calculating the shortest path from the positioning point in the target aneurysm annotation result to the pixel point in the foreground area of ​​the blood vessel segmentation result.

[0047] Next, at step S402, the binary image and distance map of the blood vessel segmentation are input into the trained segmentation model to perform aneurysm segmentation to obtain the aneurysm segmentation result. That is, the binary image and distance map of the blood vessel segmentation are directly input into the trained segmentation model, and the aneurysm segmentation results of different categories are obtained by output. As an example, non-aneurysms and target aneurysms corresponding to labels 1 and 2 can be output; or non-aneurysms, other aneurysms, and target aneurysms corresponding to labels 1, 2, and 3 can also be output.

[0048] Based on the above, it can be seen that the embodiment of the present application extracts the vascular segmentation results and annotates the aneurysms on the multimodal data, so as to construct the label data by combining the vascular related features and the aneurysm annotation results at the same time, and adds the distance feature to train the segmentation model. Based on this, it is possible to overcome the problem that the image features cannot be aligned when the aneurysm is directly segmented from the image, and it is possible to more accurately annotate the aneurysm and better distinguish the target aneurysm from other aneurysms to improve the segmentation accuracy of small aneurysms. Furthermore, the embodiment of the present application also uses more detailed annotations to more accurately distinguish the target aneurysm from other aneurysms to further improve the segmentation accuracy.

[0049] Figure 5 is an exemplary structural block diagram of a device 500 according to an embodiment of the present application. It is understood that the device 500 may include the apparatus of the embodiment of the present application, and the device implementing the solution of the present application may be a single device (such as a computing device) or a multi-function device including various peripheral devices.

[0050] like Figure 5 As shown in , the device of the present application may also include a central processing unit or central processing unit ("CPU") 511, which may be a general-purpose CPU, a dedicated CPU, or other information processing and program execution unit. Further, the device 500 may also include a large-capacity memory 512 and a read-only memory ("ROM") 513, wherein the large-capacity memory 512 may be configured to store various types of data, including various multimodal data, blood vessel segmentation results, distance maps and aneurysm annotation results, algorithm data, intermediate results, and various programs required to run the device 500. The ROM 513 may be configured to store data and instructions required for power-on self-test of the device 500, initialization of various functional modules in the system, basic input / output drivers of the system, and booting the operating system.

[0051] Optionally, the device 500 may also include other hardware platforms or components, such as the tensor processing unit ("TPU") 514, graphics processing unit ("GPU") 515, field programmable gate array ("FPGA") 516, and machine learning unit ("MLU") 517 shown. It is understood that although a variety of hardware platforms or components are shown in the device 500, they are merely exemplary and not restrictive, and those skilled in the art may add or remove corresponding hardware according to actual needs. For example, the device 500 may only include a CPU, related storage devices, and interface devices to implement the method for training a segmentation model for segmenting an aneurysm or the method for segmenting an aneurysm of the present application.

[0052] In some embodiments, in order to facilitate the transmission and interaction of data with an external network, the device 500 of the present application further includes a communication interface 518, so that it can be connected to a local area network / wireless local area network ("LAN / WLAN") 505 through the communication interface 518, and then connected to a local server 506 or to the Internet ("Internet") 507 through the LAN / WLAN. Alternatively or additionally, the device 500 of the present application can also be directly connected to the Internet or a cellular network through the communication interface 518 based on wireless communication technology, such as wireless communication technology based on the third generation ("3G"), the fourth generation ("4G") or the fifth generation ("5G") of the generation. In some application scenarios, the device 500 of the present application can also access the server 508 and the database 509 of the external network as needed to obtain various known algorithms, data and modules, and can remotely store various data, such as various types of data or instructions for presenting, for example, multimodal data, blood vessel segmentation results, distance maps and aneurysm annotation results.

[0053] The peripheral devices of the device 500 may include a display device 502, an input device 503, and a data transmission interface 504. In one embodiment, the display device 502 may include, for example, one or more speakers and / or one or more visual displays, which are configured to train the segmentation model for segmenting aneurysms of the present application or to perform voice prompts and / or image video display for segmenting aneurysms. The input device 503 may include other input buttons or controls such as a keyboard, a mouse, a microphone, a gesture capture camera, etc., which are configured to receive input of audio data and / or user instructions. The data transmission interface 504 may include, for example, a serial interface, a parallel interface or a universal serial bus interface ("USB"), a small computer system interface ("SCSI"), a serial ATA, a FireWire ("FireWire"), a PCI Express, and a high-definition multimedia interface ("HDMI"), etc., which are configured for data transmission and interaction with other devices or systems. According to the solution of the present application, the data transmission interface 504 can receive multimodal data collected from CT and MRI equipment, and transmit data or results including multimodal data or various other types to the device 500.

[0054] The CPU 511, mass storage 512, ROM 513, TPU 514, GPU 515, FPGA 516, MLU 517 and communication interface 518 of the device 500 of the present application can be interconnected through a bus 519, and data interaction with peripheral devices can be achieved through the bus. In one embodiment, through the bus 519, the CPU 511 can control other hardware components in the device 500 and its peripheral devices.

[0055] Combination of the above Figure 5 The device that can be used to perform the present application is described. It should be understood that the device structure or architecture here is only exemplary, and the implementation method and implementation entity of the present application are not limited thereto, but can be changed without departing from the spirit of the present application.

[0056] According to the above description in combination with the accompanying drawings, those skilled in the art can also understand that the embodiments of the present application can also be implemented by software programs. Therefore, the present application also provides a computer-readable storage medium, on which computer-readable instructions for training a segmentation model for segmenting an aneurysm or for segmenting an aneurysm are stored. When the computer-readable instructions are executed by one or more processors, they can be used to implement the present application in combination with the accompanying drawings. Figure 1 The method described in the present invention for training a segmentation model for segmenting an aneurysm; or implementing the present invention in combination with the attached Figure 4 A method for segmenting an aneurysm is described.

[0057] It should be noted that although the operations of the method of the present application are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0058] It should be understood that when the terms "first", "second", "third" and "fourth" are used in the claims, the specification and the drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0059] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this application specification and claims, unless the context clearly indicates otherwise, the singular forms of "a", "an" and "the" are intended to include plural forms. It should also be further understood that the term "and / or" used in this application specification and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0060] Although the implementation methods of the present application are as above, the contents described are only examples adopted to facilitate the understanding of the present application, and are not intended to limit the scope and application scenarios of the present application. Any technician in the technical field described in the present application can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of patent protection of the present application shall still be subject to the scope defined in the attached claims.

[0061] In addition, the collection and acquisition of various data in this application complies with relevant laws and regulations and is authorized by the data provider. Any organization or individual that needs to obtain external data must obtain authorization in accordance with the law and ensure data security. It is not allowed to illegally collect, use, process, or transmit unauthorized or unprotected data, or to illegally buy, sell, provide, or disclose unauthorized or unprotected data.

Claims

1. A method for training a segmentation model for segmenting an aneurysm, comprising: Acquiring multimodal imaging data, wherein the multimodal imaging data includes digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data; Performing blood vessel segmentation and marking target aneurysms based on the multimodal image data to obtain blood vessel segmentation results and target aneurysm marking results; Constructing a distance map and label data according to the blood vessel segmentation result and the target aneurysm labeling result; as well as The blood vessel segmentation result, the distance map and the label data are input into a segmentation model to perform aneurysm segmentation to obtain a target aneurysm, so as to train the segmentation model for segmenting aneurysms.

2. The method according to claim 1, wherein the distance map is constructed by: arbitrarily selecting a positioning point in the target aneurysm marking result; and The shortest path from each pixel point in the foreground area of ​​the blood vessel segmentation result to the positioning point is calculated to construct the distance map, wherein the shortest path does not pass through the background area of ​​the blood vessel segmentation result.

3. The method according to claim 2, wherein the tag data is constructed by the following operations: Setting corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm annotation result as first label data; and The corresponding pixel point located in the target aneurysm labeling result where the positioning point is located is set as the second label data.

4. The method according to claim 3, wherein inputting the blood vessel segmentation result, the distance map and the label data into a segmentation model to perform aneurysm segmentation to obtain a target aneurysm, so as to train the segmentation model for segmenting aneurysms comprises: The blood vessel segmentation result, the distance map, the first label data and the second label data are input into the segmentation model to perform aneurysm segmentation, and different categories are output to obtain target aneurysms, so as to train the segmentation model for segmenting aneurysms.

5. The method according to claim 2, wherein the tag data is constructed by the following operations: Setting corresponding pixel points located in the foreground area of ​​the blood vessel segmentation result and not located in the target aneurysm annotation result as first label data; setting corresponding pixel points located in the target aneurysm annotation result excluding the positioning point as second label data; and The corresponding pixel point located in the target aneurysm labeling result where the positioning point is located is set as the third label data.

6. The method according to claim 5, wherein inputting the blood vessel segmentation result, the distance map and the label data into a segmentation model to perform aneurysm segmentation to obtain a target aneurysm, so as to train the segmentation model for segmenting aneurysms further comprises: The blood vessel segmentation result, the distance map, the first label data, the second label data and the third label data are input into the segmentation model for aneurysm segmentation, and different categories are output to obtain target aneurysms to train the segmentation model for segmenting aneurysms.

7. The method according to claim 1, wherein the segmentation model comprises a Unet model, a FPN model or a PSPNet model.

8. A method for segmenting an aneurysm, comprising: Acquiring image data to be segmented and extracting a binary image and a distance image of blood vessel segmentation based on the image data to be segmented; as well as The binary image of the blood vessel segmentation and the distance image are input into the segmentation model trained according to the method described in any one of claims 1 to 7 to perform aneurysm segmentation to obtain a target aneurysm.

9. A device comprising: processor; as well as A memory having computer instructions for training a segmentation model for segmenting an aneurysm stored thereon, which, when executed by a processor, implement the method according to any one of claims 1 to 7; or having computer instructions for segmenting an aneurysm stored thereon, which, when executed by a processor, implement the method according to claim 8.

10. A computer-readable storage medium storing computer program instructions for training a segmentation model for segmenting an aneurysm, wherein the computer program instructions, when executed by one or more processors, implement the method according to any one of claims 1-7; or storing computer program instructions for segmenting an aneurysm, wherein the computer program instructions, when executed by one or more processors, implement the method according to claim 8.

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