Method, device and storage medium for training a segmentation model for segmenting aneurysms

Abstract

The present application discloses a method, device, and storage medium for training a segmentation model for segmenting aneurysms. The method comprises: acquiring multimodal image data, wherein the multimodal image data includes digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data; performing vessel segmentation and labeling aneurysms based on the multimodal image data to obtain vessel segmentation results and aneurysm labeling results; constructing labeling data based on the vessel segmentation results and the aneurysm labeling results; and inputting the vessel segmentation results and the labeling data into a segmentation model to perform aneurysm segmentation to obtain an aneurysm segmentation result, thereby training the segmentation model for segmenting aneurysms. Utilizing the solution of the present application, the segmentation accuracy of the segmentation model can be improved to obtain more accurate aneurysm segmentation results.

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 angiography ("CTA") or magnetic resonance angiography ("MRA"). At present, for the segmentation of aneurysms, aneurysms are directly labeled in the image data, and the labeled images are input into the segmentation model for aneurysm segmentation. However, the size of aneurysms in different imaging examinations varies, and direct labeling based on image data cannot balance the characteristics of aneurysms of different sizes, 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 more accurate aneurysm segmentation results. Summary of the Invention

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

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

[0006] In some embodiments, foreground pixels on the blood vessel segmentation result that are not on the aneurysm annotation result are set as first label data; the shortest distance between the foreground pixel and the background pixel on the blood vessel segmentation result is calculated; and second label data or third label data is set based on the shortest distance and the aneurysm annotation result to construct the label data.

[0007] In other embodiments, setting the second label data or the third label data based on the shortest distance and the aneurysm annotation result includes: comparing the shortest distance with a distance threshold and determining whether the corresponding foreground pixel point is located within the aneurysm annotation result; and setting the second label data or the third label data based on the comparison result of the shortest distance with the distance threshold and the determination result of whether the corresponding foreground pixel point is located within the aneurysm annotation result.

[0008] In some other embodiments, setting the second label data or the third label data based on the comparison result of the shortest distance and the distance threshold and the judgment result of whether the corresponding foreground pixel point is located within the aneurysm annotation result includes: in response to the shortest distance being less than or equal to the distance threshold and the corresponding foreground pixel point being located within the aneurysm annotation result, setting the corresponding foreground pixel point to the second label data; or in response to the shortest distance being greater than the distance threshold and the corresponding foreground pixel point being located within the aneurysm annotation result, setting the corresponding foreground pixel point to the third label data.

[0009] In some other embodiments, the blood vessel segmentation result and the label data are input into a segmentation model to perform aneurysm segmentation to obtain an aneurysm segmentation result, so as to train the segmentation model for segmenting aneurysms, including: inputting the blood vessel segmentation result, 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 an aneurysm segmentation result, so as to train the segmentation model for segmenting aneurysms.

[0010] In some further embodiments, it also includes: calculating the proportion of the third label data in the blood vessel segmentation result; and adjusting the training weights of the first label data, the second label data and the third label data in training the segmentation model according to the proportion, so as to optimize the training of the segmentation model for segmenting aneurysms.

[0011] In some further embodiments, the segmentation model includes a Unet model, an 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 of blood vessel segmentation based on the image data to be segmented; and inputting the binary 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 an aneurysm segmentation result.

[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 aneurysms are stored, and when the computer instructions are executed by the processor, multiple embodiments of the aforementioned first aspect are implemented; or computer instructions for segmenting aneurysms are stored, and when the computer instructions are executed by the processor, one embodiment of the aforementioned 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 aneurysms, which, when executed by one or more processors, implement multiple embodiments of the aforementioned first aspect; or storing computer instructions for segmenting aneurysms, which, when executed by a processor, implement one embodiment of the aforementioned second aspect.

[0015] By using the above-provided method for training a segmentation model for segmenting aneurysms, the present embodiment utilizes multimodal image data and performs vascular segmentation and aneurysm labeling on the multimodal image data to obtain vascular segmentation results and aneurysm labeling results. Label data is then constructed by combining the vascular segmentation results and aneurysm labeling results from different image data. The vascular segmentation results are consistent across different modal data. Thus, the present embodiment can more accurately label aneurysms based on vascular morphological features and aneurysm labeling, avoiding the problem of direct labeling in the image data and the inability to align the image features of different aneurysms, thereby improving the segmentation accuracy of the segmentation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. 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 illustrating a method for training a segmentation model for segmenting an aneurysm according to an embodiment of the present application;

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

[0020] Figure 4is an exemplary flow chart illustrating 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 this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0023] It should be understood that the terms "include" and "comprising" used in the description and claims of this 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 specification are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this specification and claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" as used in this specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0025] As used in this specification and claims, the term "if" can 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" can 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 will be described in detail below with reference to the accompanying drawings.

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

[0028] First, in step S101, multimodal imaging data is acquired. This multimodal imaging data may include digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data. Digital subtraction angiography data is the gold standard for screening and diagnosis of various vascular diseases, including aneurysm diagnosis and measurement, while CT angiography data and magnetic resonance angiography data can be used for aneurysm screening and follow-up. By using these three modal data for segmentation model training, the present embodiment can significantly improve the performance of the segmentation model.

[0029] Then, at step S102, blood vessel segmentation is performed based on the multimodal imaging data and aneurysms are labeled to obtain blood vessel segmentation results and aneurysm labeling results. In some embodiments, image processing methods or deep learning methods can 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 can be based on an existing trained segmentation model to perform blood vessel segmentation to obtain a blood vessel segmentation result, and this application does not impose any restrictions on this. In some implementation scenarios, the aforementioned aneurysm labeling can be performed manually or automatically to label the aneurysm area in the blood vessel segmentation result to obtain an aneurysm labeling result.

[0030] Based on the vessel segmentation results and aneurysm annotation results obtained above, in step S103, label data is constructed based on the vessel segmentation results and aneurysm annotation results. The consistency of the vessel segmentation results across different modalities facilitates aneurysm morphological analysis, leading to more accurate aneurysm annotation. Thus, constructing label data based on the vessel segmentation results and aneurysm annotation results can improve the accuracy of aneurysm annotation, thereby enhancing the precision of subsequent segmentation model training.

[0031] In some embodiments, the foreground pixel points on the blood vessel segmentation result that are not on the aneurysm annotation result can be set as the first label data, and then the shortest distance between the foreground pixel points and the background pixel points on the blood vessel segmentation result is calculated, and then the second label data or the third label data is set according to the shortest distance and the aneurysm annotation result to construct the label data. That is, the blood vessel pixel points are first set as the first label data, and then the second and third label data are set in combination with the shortest distance and the aneurysm annotation result. The first label data corresponds to non-aneurysm. In some implementation scenarios, the aforementioned shortest distance can be, for example, the Euclidean distance between the foreground pixel points and the background pixel points. Preferably, the aforementioned distance threshold can be, for example, 2 mm. It can be understood that by calculating the shortest distance, large aneurysms and small aneurysms can be distinguished more accurately.

[0032] In some embodiments, the shortest distance can be compared with a distance threshold to determine whether the corresponding foreground pixel is located within the aneurysm annotation result, and the second label data or the third label data can be set based on the comparison result of the shortest distance and the distance threshold and the determination result of whether the corresponding foreground pixel is located within the aneurysm annotation result. More specifically, in some embodiments, in response to the shortest distance being less than or equal to the distance threshold and the corresponding foreground pixel being located within the aneurysm annotation result, the second label data is set for the corresponding foreground pixel; or in response to the shortest distance being greater than the distance threshold and the corresponding foreground pixel being located within the aneurysm annotation result, the third label data is set for the corresponding foreground pixel.

[0033] Specifically, the algorithm first determines whether the shortest distance between a foreground pixel and a background pixel in the vessel segmentation result is greater than a distance threshold and whether the corresponding foreground pixel is within the aneurysm annotation result. If the shortest distance is less than or equal to the distance threshold and the corresponding foreground pixel is within the aneurysm annotation result, the foreground pixel is assigned the second label data. If the shortest distance between a foreground pixel and a background pixel is greater than the distance threshold and the corresponding foreground pixel is within the aneurysm annotation result, the foreground pixel is assigned the third label data. In other words, both the second and third label data correspond to an aneurysm.

[0034] Furthermore, at step S104, the vessel segmentation results and label data are input into a segmentation model to perform aneurysm segmentation to obtain an aneurysm segmentation result, thereby training the segmentation model for aneurysm segmentation. In some embodiments, the vessel segmentation results, first label data, second label data, and third label data can be input into the segmentation model for aneurysm segmentation, and different categories can be output to obtain aneurysm segmentation results to train the segmentation model for aneurysm segmentation. That is, the vessel segmentation results are used as input data, and the first, second, and third label data are used as labels to train the segmentation model for aneurysm segmentation. In some implementation scenarios, the aforementioned segmentation model can be, for example, a Unet model, an FPN model, or a PSPNet model.

[0035] In combination with the above description, it can be seen that the embodiment of the present application performs blood vessel segmentation and aneurysm labeling on digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data to obtain blood vessel segmentation results and aneurysm labeling results, and at the same time, the label data constructed by combining the blood vessel segmentation results and aneurysm labeling results is used to train the segmentation model. Based on this, it is possible to avoid the problem of image feature alignment when directly segmenting aneurysms from images, thereby improving the accuracy of aneurysm identification. Furthermore, by calculating the shortest distance between foreground points and background points, the embodiment of the present application can more accurately distinguish between large aneurysms and small aneurysms, thereby improving the accuracy of aneurysm identification.

[0036] Figure 2 FIG. 1 is an exemplary flow chart showing the overall process of training a segmentation model for segmenting aneurysms 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 .

[0037] like Figure 2 As shown in FIG, in step S201, digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data are collected. Then, in step S202, blood vessel segmentation is performed based on the aforementioned three multimodal imaging 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. In step S203, aneurysms are annotated in the blood vessel segmentation result to obtain an aneurysm annotation result. In particular, for foreground pixels in the blood vessel segmentation result that are not on the aneurysm annotation result, in step S204, the foreground pixel is set as the first label data.

[0038] Furthermore, at step S205, the shortest distance between the foreground pixel and the background pixel on the blood vessel segmentation result is calculated. As mentioned above, the shortest distance can be, for example, the Euclidean distance. At steps S206 and S207, it is determined whether the shortest distance is greater than a distance threshold. Preferably, the aforementioned distance threshold can be, for example, 2 mm. When the shortest distance is not greater than the distance threshold and when the foreground pixel is located within the aneurysm annotation result, at step S208, the corresponding foreground pixel is set to the second label data. When the shortest distance is greater than the distance threshold and the foreground pixel is located within the aneurysm annotation result, at step S209, the foreground pixel is set to the third label data to construct the label data. As an example, the aforementioned first, second, and third label data can be represented by 1, 2, and 3, respectively, where 2 and 3 both correspond to aneurysms.

[0039] Based on the blood vessel segmentation result and the first, second and third label data obtained above, at step S210, the blood vessel segmentation result, the first label data, the second label data and the third label data are input into the segmentation model for training to obtain a trained segmentation model.

[0040] In some embodiments, embodiments of the present application further involve calculating the proportion of the first label data in the blood vessel segmentation results, and adjusting the training weights of the first label data, the second label data, and the third label data in the training segmentation model based on the proportions to optimize the training of the segmentation model used to segment aneurysms. Based on this, the training process can be further optimized to further improve the accuracy of the segmentation model. Specifically, in some implementation scenarios, the training weights of each label data can be set based on the proportion of data within a preset range of values.

[0041] As an example, assume that the number of pixel points of the third label data is N3, the number of pixel points of the second label data is N2, and the number of pixel points of the first label data is N1. The proportion of the third label data can be obtained as N3 / (N1 + N2 + N3). Further, the total number of all data is denoted as num, and the aforementioned preset range values can be, for example, (0.1, 0.4, 1). In this scenario, when the number of data with N3 / (N1 + N2 + N3) > 1 is num1, the data ratio ratio1 in this interval is num1 / num; when the number of data with 0.4 < N3 / (N1 + N2 + N3) <= 1 is num2, the data ratio ratio2 in this interval is num2 / num; when the number of data with 0.1 < N3 / (N1 + N2 + N3) <= 0.4 is num3, the data ratio ratio3 in this interval is num3 / num. In the training of the segmentation model, the training weights corresponding to the first label data, the second label data, and the third label data can be set to 1 / ratio1, 1 / ratio2, and 1 / ratio3 respectively. Based on this, the weight of the aneurysm with a larger proportion in the blood vessel can be reduced, while the weight of the aneurysm with a smaller proportion can be increased, so as to balance the volume difference between large aneurysms and small aneurysms, and further improve the accuracy of the segmentation model.

[0042] Figure 3 is another exemplary flowchart showing the overall process of training a segmentation model for aneurysm segmentation according to an embodiment of the present application. It should be understood that, Figure 3 is another specific implementation of the method 100 above, so the descriptions made above about Figure 1 also apply to Figure 1 here. Figure 3 .

[0043] As Figure 3 shown, at step S301, digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data are collected. Then, at step S302, based on the foregoing three types of multimodal image data, blood vessel segmentation is performed to obtain a blood vessel segmentation result. In some embodiments, blood vessel segmentation can be performed, for example, through edge detection, morphology, or an existing segmentation model to obtain a blood vessel segmentation result. At step S303, aneurysm annotation is performed on the blood vessel segmentation result to obtain an aneurysm annotation result. According to the obtained blood vessel segmentation result and aneurysm annotation result, at step S304, the first label data, the second label data, and the third label data are constructed based on the blood vessel segmentation result and the aneurysm annotation result. Among them, more details about constructing the first label data, the second label data, and the third label data can be referred to the descriptions made above in Figure 1 , Figure 2 herein, and the present application will not elaborate further.

[0044] After obtaining the aforementioned vessel segmentation results and label data, in step S305, the vessel segmentation results, first label data, second label data, and third label data are input into the segmentation model for training. In step S306, the proportion of the third label data in the vessel segmentation results is calculated. Next, in step S307, the proportion of data within a preset range is calculated, such as the ratios 1, 2, and 3 obtained above. Furthermore, in step S308, training weights are set for each label data to train the segmentation model and obtain a trained segmentation model. For example, the training weights for the first label data, second label data, and third label data are set to 1 / ratio1, 1 / ratio2, and 1 / ratio3, respectively.

[0045] Figure 4 FIG. 4 is an exemplary flow chart 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 of blood vessel segmentation based on the image data to be segmented; step S402: inputting the binary image of blood vessel segmentation into the trained segmentation model to perform aneurysm segmentation to obtain an aneurysm segmentation result.

[0046] In step S401, image data to be segmented is obtained and a binary image of blood vessel segmentation is extracted based on the image data to be segmented. As mentioned above, the binary image of blood vessel segmentation can be extracted by methods such as image edge detection, image morphology, or deep learning.

[0047] Next, at step S402, the binary image of the segmented blood vessels is input into the trained segmentation model for aneurysm segmentation, thereby obtaining an aneurysm segmentation result. Specifically, the binary image of the segmented blood vessels is directly input into the trained segmentation model, and the output is aneurysm segmentation result of different categories. For example, if the third and second labels output correspond to categories 3 and 2, the image is an aneurysm. If the first label output corresponds to category 1, the image is not an aneurysm.

[0048] Based on the above, the embodiments of the present application extract vascular segmentation results and annotate aneurysms from multimodal data, thereby simultaneously combining vascular-related features and aneurysm annotation results to construct label data and train the segmentation model. Based on this, the problem of image feature alignment when directly segmenting aneurysms from images can be overcome, thereby improving aneurysm segmentation accuracy. Furthermore, the embodiments of the present application also train the segmentation model by adjusting the training weights of each data to balance the volume difference between large aneurysms and small aneurysms, thereby further optimizing the performance of the segmentation model.

[0049] Figure 5is 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 multifunctional 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 ("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 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 further 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 will be understood that although multiple hardware platforms or components are shown in the device 500, these are merely exemplary and non-limiting, 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, 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, which can be connected to a local area network / wireless local area network ("LAN / WLAN") 505 via the communication interface 518, and then connected to a local server 506 or the Internet ("Internet") 507 via 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 via the communication interface 518 based on wireless communication technology, such as wireless communication technology based on the third generation ("3G"), fourth generation ("4G"), or fifth generation ("5G") generations. In some application scenarios, the device 500 of the present application can also access a server 508 and a database 509 of an external network as needed to obtain various known algorithms, data, and modules, and can remotely store various data, such as various data or instructions for presenting multimodal data, vessel segmentation results, 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 provide voice prompts and / or image video displays 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 via a bus 519 and can interact with peripheral devices via the bus. In one embodiment, the CPU 511 can control other hardware components in the device 500 and its peripheral devices via the bus 519.

[0055] Combination of the above Figure 5The devices that can be used to implement the present application are described. It should be understood that the device structures or architectures herein are merely exemplary, and the implementation methods and implementation entities of the present application are not limited thereto, but can be modified 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, which stores computer-readable instructions for training a segmentation model for segmenting an aneurysm or for segmenting an aneurysm. 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 for training a segmentation model for segmenting aneurysms described in the present application; or implementing the method 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 present method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in that particular order, or that all of the operations shown must be performed to achieve the desired results. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be broken down into multiple steps.

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

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

[0060] Although the implementation methods of this application are as described above, the contents are only examples adopted to facilitate understanding of this application and are not intended to limit the scope and application scenarios of this application. Any technician in the technical field described in this application can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in this application, but the scope of patent protection of this application shall still be based on the scope defined by 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. They must not illegally collect, use, process, or transmit unauthorized or unprotected data, nor 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 annotating aneurysms based on the multimodal image data to obtain blood vessel segmentation results and aneurysm annotation results; Constructing label data based on the blood vessel segmentation result and the aneurysm annotation result, the label data including first label data, second label data, and third label data, wherein the first label data is used to represent non-aneurysm; the second label data and the third label data are used to represent different types of aneurysms; and Inputting the blood vessel segmentation result and the label data into a segmentation model to perform aneurysm segmentation to obtain an aneurysm segmentation result, so as to train the segmentation model for segmenting aneurysms; Constructing label data according to the blood vessel segmentation result and the aneurysm annotation result includes: Setting foreground pixels on the blood vessel segmentation result and not on the aneurysm annotation result as first label data; Calculating the shortest distance between a foreground pixel and a background pixel on the blood vessel segmentation result; and Comparing the shortest distance with a distance threshold and determining whether the corresponding foreground pixel is located within the aneurysm annotation result; In response to the shortest distance being less than or equal to the distance threshold, and the corresponding foreground pixel being located within the aneurysm annotation result, setting the corresponding foreground pixel as the second label data; or In response to the shortest distance being greater than the distance threshold and the corresponding foreground pixel being located within the aneurysm annotation result, the corresponding foreground pixel is set as the third label data to construct the label data.

2. The method according to claim 1, wherein inputting the blood vessel segmentation result and the label data into a segmentation model to perform aneurysm segmentation to obtain an aneurysm segmentation result, so as to train the segmentation model for segmenting aneurysms, comprises: The blood vessel segmentation result, 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 aneurysm segmentation results to train the segmentation model for aneurysm segmentation.

3. The method according to claim 2, further comprising: Calculating the proportion of the third label data in the blood vessel segmentation result; as well as The training weights of the first label data, the second label data, and the third label data in training the segmentation model are adjusted according to the proportions to optimize the training of the segmentation model for segmenting aneurysms.

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

5. A method for segmenting an aneurysm, comprising: Acquiring image data to be segmented and extracting a binary image of blood vessel segmentation based on the image data to be segmented; as well as The binary image of the blood vessel segmentation is input into the segmentation model trained according to the method according to any one of claims 1 to 4 to perform aneurysm segmentation to obtain an aneurysm segmentation result.

6. An apparatus for training a segmentation model for segmenting an aneurysm, comprising: processor; as well as A memory having computer instructions for training a segmentation model for segmenting an aneurysm stored thereon, wherein when the computer instructions are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

7. An apparatus for segmenting an aneurysm, comprising: processor; as well as A memory having stored thereon computer instructions for segmenting an aneurysm, which, when executed by a processor, enable implementation of the method according to claim 5.

8. 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 to 4; 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 5.

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