Method, device and storage medium for training a segmentation model for segmenting blood vessel segments

Abstract

This application discloses a method, device, and storage medium for training a segmentation model for segmenting vascular segments. The method includes: acquiring multimodal imaging data, including digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data; performing vascular segmentation and extracting vascular surfaces based on the multimodal imaging data to obtain vascular surface data; annotating positioning points and vascular segments on the vascular surface data, and inputting the annotated vascular surface data into a positioning model for training; calculating distance features on the vascular surface data based on the positioning point information; and inputting the vascular surface data, distance features, and vascular segment annotations into the segmentation model to perform vascular segmentation to obtain vascular segments, thereby training the segmentation model for segmenting vascular segments. Using the solution of this application, the segmentation accuracy of the segmentation model can be improved, resulting in more accurate vascular segmentation results.

Description

Technical Field

[0001] The present application generally relates to the technical field of blood vessel segmentation, and more particularly to a method for training a segmentation model for segmenting blood vessel segments, a method, a device, and a computer-readable storage medium for segmenting blood vessel segments. Background Art

[0002] To meet the needs of research and clinical treatment, image processing and segmentation methods based on medical image analysis are often required to segment vascular images into multiple segments to obtain vascular network information for disease diagnosis and prognosis. For example, segmenting intracranial arteries can help doctors accurately diagnose the location of arteriosclerosis and cerebrovascular disease and make a preliminary assessment of the prognosis. Currently, vascular segmentation is performed by directly annotating the segments in the image data and then inputting the annotated images into a segmentation model for segmentation, resulting in low segmentation accuracy.

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

[0005] In a first aspect, the present application provides a method for training a segmentation model for segmenting vascular segments, comprising: acquiring multimodal image data, wherein the multimodal image data includes digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data; performing vascular segmentation and extracting vascular surfaces based on the multimodal image data to obtain vascular surface data; annotating positioning points and vascular segments on the vascular surface data, and inputting the annotated vascular surface data into the positioning model for training to output positioning point information; calculating distance features on the vascular surface data based on the positioning point information; and inputting the vascular surface data, the distance features, and the vascular segment annotations into the segmentation model for performing vascular segmentation to obtain vascular segments, so as to train the segmentation model for segmenting vascular segments.

[0006] In some embodiments, the positioning points include at least connection points at key blood vessels in the blood vessel surface data; and the positioning point information includes coordinate information of the positioning points or index information of the positioning points.

[0007] In some other embodiments, calculating the distance feature on the blood vessel surface data based on the positioning point information includes: calculating, based on the positioning point information, projection values ​​of a vector formed between a target point and a target positioning point on the blood vessel surface data on vectors formed between the target positioning point and the remaining positioning points, so as to calculate the distance feature on the blood vessel surface data.

[0008] In some further embodiments, calculating the distance feature on the blood vessel surface data based on the positioning point information further includes: calculating the projection value of the vector formed between the target point and the target positioning point on the blood vessel surface data on the target vector based on the positioning point information, so as to calculate the distance feature on the blood vessel surface data; wherein the target vector is perpendicular to the vectors formed between the target positioning point and the remaining positioning points.

[0009] In some further embodiments, it further includes: calculating the geodesic distance on the blood vessel surface data based on the positioning point information; adding the geodesic distance to the distance feature to obtain a new distance feature; and inputting the blood vessel surface data, the new distance feature and the blood vessel segmentation annotation into the segmentation model to perform blood vessel segmentation to obtain blood vessel segments, so as to train the segmentation model for segmenting blood vessel segments.

[0010] In some other embodiments, calculating the geodesic distance on the blood vessel surface data based on the positioning point information includes: calculating the original geodesic distance from the target point on the blood vessel surface data to each positioning point; and / or calculating the shortest geodesic distance from the target point on the blood vessel surface data to each positioning point.

[0011] In some further embodiments, the positioning model and the segmentation model both include a point cloud processing model or an image processing model.

[0012] In a second aspect, the present application provides a method for segmenting blood vessel segments, comprising: acquiring image data to be segmented and extracting blood vessel surface data based on the image data to be segmented; and inputting the blood vessel surface data into a segmentation model trained by one or more embodiments of the first aspect to perform blood vessel segmentation to obtain a blood vessel 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 vascular segments 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 vascular segments 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 vascular segments, which, when executed by one or more processors, implement multiple embodiments of the aforementioned first aspect; or storing computer instructions for segmenting vascular segments, which, when executed by a processor, implement one embodiment of the aforementioned second aspect.

[0015] Through the above-provided method for training a segmentation model for segmenting vascular segments, the present embodiment of the present invention obtains vascular surface data by performing vascular segmentation on multimodal imaging data and extracting the vascular surface. First, a positioning model is trained based on the anchor point annotations on the vascular surface data. Distance features are then calculated based on the anchor point information. Finally, a segmentation model is trained based on the vascular surface data, distance features, and vascular segment annotations to output vascular segmentation results. Based on this, the present embodiment of the present invention improves the recognition accuracy of vascular segments by incorporating distance features to quantify vascular feature information, thereby enhancing the segmentation accuracy of the vascular 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 a blood vessel segment 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 blood vessel segments 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 blood vessel segments according to an embodiment of the present application;

[0020] Figure 4 is an exemplary schematic diagram showing blood vessel surface data according to an embodiment of the present application;

[0021] Figure 5 is an exemplary schematic diagram showing a distance feature map obtained by calculating projection values ​​according to an embodiment of the present application;

[0022] Figure 6 is an exemplary schematic diagram illustrating a distance feature map obtained by calculating a geodesic distance according to an embodiment of the present application;

[0023] Figure 7 is an exemplary flowchart illustrating a method for segmenting a blood vessel segment according to an embodiment of the present application;

[0024] Figure 8 is a block diagram showing an exemplary structure of a device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] 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 them. 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] The specific implementation of the present application will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 FIG. 1 is an exemplary flow chart illustrating a method 100 for training a segmentation model for segmenting a blood vessel segment according to an embodiment of the present application. The segmentation model includes a positioning model and a segmentation model. 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 extracting blood vessel surfaces based on the multimodal image data to obtain blood vessel surface data; step S103: marking positioning points and blood vessel segments on the blood vessel surface data, and inputting the marked blood vessel surface data into a positioning model for training to output positioning point information; step S104: calculating distance features on the blood vessel surface data based on the positioning point information; and step S105: inputting the blood vessel surface data, distance features, and blood vessel segmentation annotations into a segmentation model for blood vessel segmentation to obtain blood vessel segments, so as to train the segmentation model for segmenting blood vessel segments.

[0031] First, in step S101, multimodal imaging data is acquired. This multimodal imaging data may include digital subtraction angiography (DSA) data, CT angiography (CT) angiography (MRI) data, and magnetic resonance angiography (MRI) data. DSA data is the gold standard for screening and diagnosis of various vascular diseases, while CT angiography (CT) and MRI angiography (MRI) data can be used for screening and follow-up of vascular diseases. By using these three modalities of data for training a segmentation model, the present embodiment can significantly improve the performance of the segmentation model.

[0032] Next, in step S102, vessel segmentation is performed based on the multimodal image data, and the vessel surface is extracted to obtain vessel surface data. In some embodiments, vessel segmentation can be performed using image processing methods or deep learning methods, where the aforementioned image processing methods may include, but are not limited to, image edge detection and image morphology methods. The aforementioned deep learning methods can be used to perform vessel segmentation based on an existing trained segmentation model to obtain vessel segmentation results, and this application does not impose any restrictions on this. In other embodiments, based on the aforementioned vessel segmentation results, vessel surface data can be extracted by using, for example, a Marching Cubes algorithm.

[0033] Furthermore, in step S103, the vascular surface data is labeled with positioning points and vascular segments, and the labeled vascular surface data is input into a positioning model for training to output positioning point information. In some implementation scenarios, the aforementioned dynamic positioning points and vascular segments can be labeled manually or automatically. In some embodiments, the aforementioned positioning model can be, for example, a point cloud processing model (e.g., PointNet) or an image processing model (e.g., a Unet model, an FPN model, or a PSPNet model).

[0034] In some embodiments, the positioning points may include at least connection points at key blood vessels in the vascular surface data, and the positioning point information may include coordinate information of the positioning points or index information of the positioning points. That is, when marking the positioning points, they can be marked by coordinates (x, y, z) or by indexes (for example, P1, P2, P3, ..., Pn). In some implementation scenarios, when marking blood vessel segments, each blood vessel segment can be marked by, for example, 1, 2, 3, ..., n. If the pixel point is a background pixel, it is marked as 0. It can be understood that the aforementioned positioning points can be added or deleted according to the specific blood vessel segmentation task, and this application does not limit the number of positioning points.

[0035] As an example, in the carotid artery system of the intracranial artery, the aforementioned positioning points can be the connection point of the left internal carotid artery (ICA) with the left middle cerebral artery (MCA) and the left anterior cerebral artery (ACA); the connection point of the left ICA with the left MCA and the left ACA; and the connection point of the right ICA with the right MCA and the right ACA. The aforementioned connections are all located at key positions of the Circle of Willis (also known as the Circle of the Cerebral Arteries), which are conducive to locating the vascular segments of the carotid artery system. In the vertebral-basilar artery system, the aforementioned positioning points can include, for example, the connection point of the basilar artery (BA) with the left posterior communicating artery (PCA) and the right PCA, which is located at a key position of the vertebral-basilar artery system, which is conducive to locating the vascular segments of the vertebral-basilar artery system.

[0036] Based on the labeled positioning points, the positioning point information can be output by inputting them into the positioning model for training. For example, if the labeled positioning point information is coordinate information, the input of the positioning model is the three coordinate values ​​(x, y, z) of each point. If the labeled positioning point information is the index of the connection point, the input of the positioning model is the category of the connection to which each point belongs. For example, if the connection point where the left ICA connects with the MCA and ACA is recorded as P1, the connection point where the right ICA connects with the MCA and ACA is recorded as P2, and the connection point where the BA connects with the left and right PCAs is recorded as P3. In this scenario, the category of the connection to which each point belongs is 0, P1, P2, or P3.

[0037] Based on the obtained positioning point information, at step S104, distance features on the blood vessel surface data are calculated based on the positioning point information. In some embodiments, projection values ​​of a vector formed between a target point and a target positioning point on the respective vectors formed between the target positioning point and the remaining positioning points are calculated based on the positioning point information to calculate the distance features on the blood vessel surface data.

[0038] For example, in an exemplary scenario, assuming that the target point on the blood vessel surface data is denoted as X, and taking the target anchor point as the connection point P3 where BA connects the left and right PCAs, the projection value of the vector P3X formed between the target point X and the target anchor point P3 onto the vector P3P1 formed between the target anchor point P3 and the anchor point P1 can be calculated to obtain a feature map, and the projection value of the vector P3X formed between the target point X and the target anchor point P3 onto the vector P3P2 formed between the target anchor point P3 and the anchor point P2 can be calculated to obtain a feature map to obtain a distance feature. Similarly, the projection values ​​of vector P1X on vectors P1P2 and P1P3 can be calculated, and the projection values ​​of vector P2X on vectors P2P1 and P2P3 can be calculated.

[0039] In other embodiments, the projection value of the vector formed between the target point and the target anchor point on the vascular surface data onto the target vector can also be calculated based on the anchor point information to calculate the distance feature on the vascular surface data; where the target vector is perpendicular to the target anchor point and the vectors formed between the remaining anchor points. For example, if the target vector perpendicular to both vectors P3P1 and P3P2 is denoted as P3P1×P3P2, the projection value of vector P3X onto the target vector P3P1×P3P2 can also be calculated as the distance feature.

[0040] Furthermore, at step S105, the vascular surface data, distance features, and vascular segmentation annotations are input into the segmentation model for vascular segmentation to obtain vascular segments, so as to train the segmentation model for segmenting vascular segments. Specifically, the vascular surface data and distance features can be used as input data, and the vascular segmentation annotations can be used as labels to train the segmentation model for segmenting vascular segments. In some implementation scenarios, the aforementioned segmentation model can also be, for example, a point cloud processing model (such as PointNet) or an image processing model (such as a Unet model, an FPN model, or a PSPNet model). In the implementation scenario, each pixel is classified according to the vascular segmentation k = 1, 2, 3, ..., n to obtain a vascular segmentation result.

[0041] As can be seen from the above description, the present embodiment first trains a positioning model based on the annotation of anchor points on the vascular surface data to obtain anchor point information. Distance features are then calculated based on the anchor point information. Finally, a segmentation model is trained based on the vascular surface data, distance features, and vascular segmentation annotations to obtain vascular segmentation results. Based on this, the quantification of vascular features by adding distance features improves the accuracy of vascular segmentation performed by the segmentation model.

[0042] Figure 2 FIG. 1 is an exemplary flow chart showing the overall process of training a segmentation model for segmenting a blood vessel segment 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 .

[0043] 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 imaging data, and at step S203, the blood vessel surface is extracted to obtain blood vessel surface data. 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. Blood vessel surface data is extracted by, for example, the MarchingCubes algorithm. Further, at step S204, positioning points and blood vessel segments are marked. For specific marking details, please refer to the above Figure 1 In step S205, the annotated blood vessel surface data is input into a positioning model for training, and positioning point information is output. In some embodiments, the positioning model can be, for example, a PointNet or PSPNet model.

[0044] Based on the aforementioned anchor point information, at step S206, multiple projection values ​​are calculated to obtain distance features. Specifically, the projection values ​​of the vector formed between the target point on the vascular surface data and the target anchor point on the vectors formed between the target anchor point and the remaining anchor points are calculated. The projection values ​​of the vector formed between the target point on the vascular surface data and the target anchor point on the target vector can also be calculated based on the anchor point information. Next, at step S207, the vascular surface data, distance features, and vascular segmentation annotations are input into a segmentation model for training, thereby training the segmentation model for segmenting vascular segments.

[0045] In some embodiments, geodesic distances can be calculated on the vessel surface data based on the anchor point information, and added to the distance feature to obtain a new distance feature. The vessel surface data, the new distance feature, and the vessel segmentation annotations are then input into a segmentation model for vessel segmentation to obtain vessel segments, thereby training the segmentation model for segmenting the vessel segments. In other words, calculating the geodesic distance to add a distance feature can better describe the connectivity between different vessel segments and contain richer feature information, thereby further improving the accuracy of the segmentation model.

[0046] In some embodiments, the original geodesic distance from a target point on the vascular surface data to each anchor point and / or the shortest geodesic distance from a target point on the vascular surface data to each anchor point can be calculated. For example, for a target point X on the vascular surface data, the original geodesic distances from target point X to anchor points P1, P2, and P3 can be calculated. Alternatively, the shortest geodesic distance from target point X to anchor points P1, P2, or P3 can be calculated.

[0047] Figure 3 It is another exemplary flow chart showing the overall training of a segmentation model for segmenting blood vessel segments 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 .

[0048] like Figure 3 As 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 imaging data, and at step S303, the blood vessel surface is extracted to obtain blood vessel surface data. 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. The blood vessel surface data is extracted by, for example, the MarchingCubes algorithm. Further, at step S304, positioning points and blood vessel segments are annotated. At step S305, the annotated blood vessel surface data is input into the positioning model for training, and the positioning point information is output.

[0049] Based on the aforementioned positioning point information, multiple projection values ​​are calculated in step S306 to obtain a distance feature. Next, in step S307, the geodesic distance is calculated and added to the distance feature to obtain a new distance feature. Furthermore, in step S308, the vessel surface data, the new distance feature, and the vessel segment annotations are input into a segmentation model for training, thereby training the segmentation model for segmenting vessel segments.

[0050] Figure 4 FIG. 1 is an exemplary schematic diagram showing blood vessel surface data according to an embodiment of the present application. Figure 4 Figures (a) and (b) in the figure show the top view and front view of the blood vessel surface data, respectively. The figures exemplarily illustrate the connection point P1 where the left ICA connects with the MCA and ACA, the connection point P2 where the right ICA connects with the MCA and ACA, and the connection point P3 where the BA connects with the left and right PCAs. These are the positioning points in the embodiment of the present application.

[0051] Figure 5 : is an exemplary schematic diagram showing a distance feature map obtained by calculating projection values ​​according to an embodiment of the present application. Figure 5 Figures (a), (b) and (c) in the figure respectively show the feature map corresponding to the projection value of vector P3X on vector P3P1, the feature map corresponding to the projection value of P3X on P3P2, and the feature map corresponding to the projection value of P3X on the target vector P3P1*P3P2.

[0052] Figure 6 FIG. 1 is an exemplary schematic diagram showing a distance feature map obtained by calculating the geodesic distance according to an embodiment of the present application. Figure 6 Figures (a), (b), (c), and (d) in the figure respectively show the feature maps corresponding to the original geodesic distances from the target point X to the positioning points P1, P2, and P3, and the distance maps corresponding to the shortest geodesic distances from the target point X to the positioning points P1, P2, or P3.

[0053] Figure 7 FIG. 7 is an exemplary flow chart illustrating a method 700 for segmenting a blood vessel segment according to an embodiment of the present application. Figure 7 As shown in , the method 700 includes: step S701: acquiring image data to be segmented and extracting blood vessel surface data based on the image data to be segmented; step S702: inputting the blood vessel surface data into the trained segmentation model to perform blood vessel segmentation to obtain a blood vessel segmentation result.

[0054] In step S701, image data to be segmented is acquired and vascular surface data is extracted based on the image data. Next, in step S702, the vascular surface data is input into a trained segmentation model to perform vascular segmentation, thereby obtaining vascular segmentation results. As previously described, the segmentation model includes a positioning model and a segmentation model. During vascular segmentation, the vascular surface data is first processed using the positioning model to obtain positioning points. Subsequently, the segmentation model is used to complete vascular segmentation, resulting in vascular segmentation results. These vascular segmentation results correspond to vascular segment categories, such as 1, 2, 3, ..., n.

[0055] As can be seen from the foregoing, the embodiments of the present application obtain vascular surface data by performing vascular segmentation on multimodal image data and extracting vascular surfaces. A positioning model is trained based on the positioning points on the vascular surface data, and distance features are calculated to quantify the feature information used to train a segmentation model. This improves the recognition accuracy of vascular segments and enhances the segmentation accuracy of the vascular segmentation model. Furthermore, the embodiments of the present application further enhance the segmentation accuracy of the segmentation model by adding distance features, resulting in more accurate vascular segmentation results.

[0056] Figure 8 is an exemplary structural block diagram of a device 800 according to an embodiment of the present application. It is understood that the device 800 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.

[0057] like Figure 8 As shown in , the device of the present application may also include a central processing unit ("CPU") 811, which may be a general-purpose CPU, a dedicated CPU, or other information processing and program execution unit. Further, the device 800 may also include a mass storage 812 and a read-only memory ("ROM") 813, wherein the mass storage 812 may be configured to store various types of data, including various multimodal data, vascular surface data, positioning point information and distance features, algorithm data, intermediate results, and various programs required to run the device 800. The ROM 813 may be configured to store data and instructions required for power-on self-test of the device 800, initialization of various functional modules in the system, basic input / output drivers of the system, and booting the operating system.

[0058] Optionally, the device 800 may also include other hardware platforms or components, such as the tensor processing unit ("TPU") 814, graphics processing unit ("GPU") 815, field programmable gate array ("FPGA") 816, and machine learning unit ("MLU") 817 shown. It will be understood that although multiple hardware platforms or components are shown in the device 800, 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 800 may only include a CPU, related storage devices, and interface devices to implement the method for training a segmentation model for segmenting a blood vessel segment or the method for segmenting a blood vessel segment of the present application.

[0059] In some embodiments, to facilitate the transmission and interaction of data with an external network, the device 800 of the present application further includes a communication interface 818, which can be connected to a local area network / wireless local area network ("LAN / WLAN") 805 via the communication interface 818, and then connected to a local server 806 or the Internet ("Internet") 807 via the LAN / WLAN. Alternatively or additionally, the device 800 of the present application can also be directly connected to the Internet or a cellular network via the communication interface 818 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 800 of the present application can also access a server 808 and a database 809 of an 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, vascular surface data, location point information, and distance features.

[0060] The peripheral devices of the device 800 may include a display device 802, an input device 803 and a data transmission interface 804. In one embodiment, the display device 802 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 the blood vessel segments of the present application or to provide voice prompts and / or image video displays for segmenting the blood vessel segments. The input device 803 may include, for example, 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 804 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 804 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 800.

[0061] The CPU 811, mass storage 812, ROM 813, TPU 814, GPU 815, FPGA 816, MLU 817, and communication interface 818 of the device 800 of the present application can be interconnected via a bus 819 and can interact with peripheral devices via the bus. In one embodiment, the CPU 811 can control other hardware components in the device 800 and its peripheral devices via the bus 819.

[0062] Combination of the above Figure 8The 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.

[0063] 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 blood vessel segments or for segmenting blood vessel segments. 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 a blood vessel segment described in the present application; or implementing the method in combination with the attached Figure 7 A method for segmenting blood vessel segments is described.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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 blood vessel segments, wherein the segmentation model includes a localization model and a segmentation model, and the method comprises: 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 extracting blood vessel surfaces based on the multimodal image data to obtain blood vessel surface data; marking positioning points and marking blood vessel segments on the blood vessel surface data, and inputting the marked blood vessel surface data into the positioning model for training to output positioning point information; Calculating distance features on the blood vessel surface data according to the positioning point information; and Inputting the blood vessel surface data, the distance features and the blood vessel segmentation annotations into the segmentation model to perform blood vessel segmentation to obtain blood vessel segments, so as to train the segmentation model for segmenting blood vessel segments. Calculating the distance feature on the blood vessel surface data according to the positioning point information includes: Projection values ​​of the vector formed between the target point and the target positioning point on the blood vessel surface data and the vectors formed between the target positioning point and the remaining positioning points are calculated respectively according to the positioning point information to calculate the distance feature on the blood vessel surface data.

2. The method according to claim 1, wherein the positioning points at least include connection points at key blood vessels in the blood vessel surface data; and the positioning point information includes coordinate information of the positioning points or index information of the corresponding positioning points.

3. The method according to claim 1, wherein calculating the distance feature on the blood vessel surface data based on the positioning point information further comprises: A projection value of a vector formed between a target point and a target positioning point on the vascular surface data is calculated based on the positioning point information to calculate the distance feature on the vascular surface data; wherein the target vector is perpendicular to the vectors formed between the target positioning point and the remaining positioning points.

4. The method according to claim 3, further comprising: Calculating the geodesic distance on the blood vessel surface data according to the positioning point information; Adding the geodesic distance to the distance feature to obtain a new distance feature; as well as The blood vessel surface data, the new distance feature and the blood vessel segmentation annotation are input into the segmentation model to perform blood vessel segmentation to obtain blood vessel segments, so as to train the segmentation model for segmenting blood vessel segments.

5. The method according to claim 4, wherein calculating the geodesic distance on the blood vessel surface data according to the positioning point information comprises: Calculating the original geodesic distance from the target point on the blood vessel surface data to each positioning point; and / or The shortest geodesic distance from the target point on the blood vessel surface data to each positioning point is calculated. The method according to claim 1 , wherein the positioning model and the segmentation model both comprise a point cloud processing model or an image processing model.

7. A method for segmenting a blood vessel segment, comprising: Acquiring image data to be segmented and extracting blood vessel surface data based on the image data to be segmented; as well as The blood vessel surface data is input into a segmentation model trained according to the method according to any one of claims 1 to 6 to perform blood vessel segmentation to obtain a blood vessel segmentation result.

8. A device comprising: processor; as well as A memory storing computer instructions for training a segmentation model for segmenting blood vessel segments, which, when executed by a processor, implement the method according to any one of claims 1 to 6; or storing computer instructions for segmenting blood vessel segments, which, when executed by a processor, implement the method according to claim 7.

9. A computer-readable storage medium storing computer program instructions for training a segmentation model for segmenting vascular segments, wherein when the computer program instructions are executed by one or more processors, the method according to any one of claims 1 to 6 is implemented; or storing computer program instructions for segmenting vascular segments, wherein when the computer program instructions are executed by one or more processors, the method according to claim 7 is implemented.

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