An individualized partitioning method and device based on brain surface geometric features, medium and product

Abstract

The application discloses an individualized partition method and device based on brain surface geometric features, a medium and a product, and relates to the technical field of neuroimaging. The method comprises the following steps: dividing each vertex into sulcus seed points, gyrus seed points and remaining vertices based on the shape index of each vertex, thereby obtaining a plurality of sulcus seed initial regions and gyrus seed initial regions; merging each connected sulcus seed initial region and each gyrus seed initial region, thereby obtaining a plurality of merged regions; determining each initial region that is not connected as an independent region; determining all vertices in the merged region and the independent region and all remaining vertices that have a number less than a preset number as transition vertices; determining the merged region and the independent region that have a number greater than and / or equal to the preset number as initial inclusion regions; iteratively including each transition vertex, thereby obtaining a personalized brain partition map, and achieving the partition of the brain. The application improves the accuracy of brain region partitioning.

Description

Technical Field

[0001] This application relates to the field of neuroimaging technology, and in particular to a method, device, medium, and product for individualized partitioning based on brain surface geometry. Background Technology

[0002] Currently, brain regionalization primarily relies on traditional brain atlases, such as Brodmann partitions. While these standardized atlases are widely used, they have the following limitations: 1) They ignore individual anatomical differences and fail to reflect individual characteristics; 2) They are mainly based on two-dimensional slices, failing to fully utilize the three-dimensional geometric information of the brain surface; 3) The manual regionalization is highly subjective and lacks objective mathematical basis; 4) They are difficult to adapt to the personalized analysis requirements of precision medicine. Therefore, traditional brain regionalization methods have relatively low accuracy in brain regionalization.

[0003] Therefore, developing an individualized partitioning method based on objective brain surface geometric features has significant scientific and practical value. Summary of the Invention

[0004] The purpose of this application is to provide a personalized partitioning method, device, medium, and product based on brain surface geometric features to solve the problem of low accuracy in brain partitioning.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a personalized partitioning method based on brain surface geometric features, including:

[0007] Acquire three-dimensional mesh data of the brain surface; the three-dimensional mesh data includes multiple vertices and multiple edges connecting two adjacent vertices;

[0008] Based on the three-dimensional mesh data, determine the shape index of each vertex;

[0009] Based on the shape index of each vertex, each vertex is divided into sulcus seed points, gyri seed points and remaining vertices;

[0010] Multiple initial regions of brain sulcus seeding are determined based on multiple adjacent brain sulcus seeding points, and multiple initial regions of brain gyri seeding are determined based on multiple adjacent brain gyri seeding points.

[0011] The connectivity between the initial seed regions of each sulcus and the connectivity between the initial seed regions of each gyrus are determined respectively; the connectivity is either connected or disconnected; connectivity includes adjacent and intersecting.

[0012] The initial regions of the brain sulci with connectivity are merged to obtain multiple merged brain sulci regions, and the initial regions of the brain gyri with connectivity are merged to obtain multiple merged brain gyri regions.

[0013] Each sulcus seed initial region with non-connectivity is defined as an independent sulcus seed region, and each gyrus seed initial region with non-connectivity is defined as an independent gyrus seed region.

[0014] All vertices in the sulcus seed merging region, gyrus seed merging region, sulcus seed independent region and gyrus seed independent region with fewer vertices than the preset number, as well as all remaining vertices, are determined as transition vertices to obtain the initial transition vertex set.

[0015] Based on each initial inclusion region, each transition vertex in the initial transition vertex set is iteratively included to obtain multiple brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partition map and realizing brain partitioning.

[0016] Optionally, based on the three-dimensional mesh data, the shape index of each vertex is determined, including:

[0017] Based on the three-dimensional mesh data, the bending parameters of each vertex are determined; the bending parameters include: Gaussian curvature and mean curvature;

[0018] Based on the curvature parameters of each vertex, determine the maximum principal vector and minimum principal vector of each vertex.

[0019] The shape index of each vertex is determined based on the maximum and minimum principal vectors of each vertex.

[0020] Optionally, based on the three-dimensional mesh data, the bending parameters of each vertex are determined, including:

[0021] Define any vertex as the target vertex;

[0022] Using the bending parameter calculation formula, the bending parameters of the target vertex are calculated based on the three-dimensional mesh data; the bending parameter calculation formula includes:

[0023]

[0024] Where K is the Gaussian curvature of the target vertex; S is the total area of ​​the region formed by the target vertex and all adjacent vertices; N is the number of adjacent vertices of the target vertex; θ i Let be the vertex angle of the triangle formed by the target vertex, the i-th adjacent vertex, and the (i+1)-th adjacent vertex. When i = N, the (i+1)-th adjacent vertex is the first adjacent vertex. i Let l be the length of the edge formed by the target vertex and the i-th adjacent vertex;i+1 k is the length of the edge formed by the target vertex and its (i+1)th adjacent vertex. i Let be the length of the edge formed by the i-th adjacent vertex and the (i+1)-th adjacent vertex of the target vertex; H is the average curvature of the target vertex; β i Let be the dihedral angle formed by the target vertex, the i-th adjacent vertex, and the (i+1)-th adjacent vertex.

[0025] Optionally, the maximum and minimum principal vectors of each vertex are determined based on the curvature parameters of each vertex, including:

[0026] Using the principal vector calculation formula, the maximum and minimum principal vectors of each vertex are calculated based on the curvature parameters of each vertex; the principal vector calculation formula includes:

[0027]

[0028] Where, k max k is the maximum principal vector; min It is the smallest principal vector.

[0029] Optionally, the shape index of each vertex is determined based on the maximum and minimum principal vectors of each vertex, including:

[0030] Using the shape index calculation formula, the shape index of each vertex is calculated based on the maximum and minimum principal vectors of each vertex; the shape index calculation formula includes:

[0031]

[0032] SI stands for shape index.

[0033] Optionally, based on the shape index of each vertex, each vertex is divided into sulcus seed points, gyral seed points, and remaining vertices, including:

[0034] Define any vertex as the current vertex;

[0035] When the shape index of the current vertex is less than -0.5, the current vertex is determined as a brain sulcus seed point;

[0036] When the shape index of the current vertex is greater than 0.5, the current vertex is determined as a gyri seed point;

[0037] When the shape index of the current vertex is between -0.5 and 0.5, the current vertex is determined as the remaining vertex.

[0038] Optionally, based on each initial inclusion region, each transition vertex in the initial transition vertex set is iteratively included to obtain multiple brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partitioning atlas, realizing brain partitioning, including:

[0039] Based on each initial inclusion region, all transition vertices in the initial transition vertex set are iteratively included multiple times to obtain individual brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partition map and achieving brain partitioning; wherein, the inclusion process at any current iteration number includes:

[0040] Determine whether each transition vertex in the pre-update transition vertex set under the current iteration number is adjacent to any vertex in the pre-update inclusion region under the current iteration number. When the current iteration number is the initial iteration number, the pre-update transition vertex set under the current iteration number is the initial transition vertex set, and the pre-update inclusion region under the current iteration number is the initial inclusion region. When the current iteration number is not the initial iteration number, the pre-update transition vertex set under the current iteration number is the updated transition vertex set under the previous iteration number, and the pre-update inclusion region under the current iteration number is the updated inclusion region under the previous iteration number. The inclusion region includes: the sulcus seed inclusion region and the gyrus seed inclusion region.

[0041] Each transition vertex with an adjacency relationship is included in the pre-update inclusion region at the current iteration number, resulting in the updated inclusion region at the current iteration number;

[0042] Remove all transition vertices that are adjacent from the set of transition vertices before the update in the current iteration number, and retain all transition vertices that are not adjacent in the set of transition vertices before the update in the current iteration number, to obtain the set of transition vertices after the update in the current iteration number.

[0043] Determine if the number of transition vertices in the updated transition vertex set at the current iteration number is 0;

[0044] If not, then determine the updated transition vertex set under the current iteration number as the transition vertex set before the update under the next iteration number, determine the updated inclusion region under the current iteration number as the inclusion region before the update under the next iteration number, update the current iteration number to the next iteration number, and return "determine whether each transition vertex in the transition vertex set before the update under the current iteration number has an adjacency relationship with any vertex in the inclusion region before the update under the current iteration number".

[0045] If so, the updated regions for each sulcus seed inclusion and each region for each gyrus seed inclusion under the current iteration number are determined as the target regions for the sulcus seed and the target regions for the gyrus seed, respectively, thereby obtaining a personalized brain partition map and realizing the partitioning of the brain.

[0046] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the individualized partitioning method based on brain surface geometric features as described in any of the preceding claims.

[0047] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the individualized partitioning method based on brain surface geometric features as described in any of the preceding claims.

[0048] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the individualized partitioning method based on brain surface geometric features as described in any of the preceding claims.

[0049] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0050] This application discloses a method, device, medium, and product for individualized partitioning based on brain surface geometric features. First, three-dimensional mesh data of the brain surface is acquired. Based on the three-dimensional mesh data, the shape index of each vertex is determined. Based on the shape index of each vertex, each vertex is divided into sulcus seed points, gyral seed points, and remaining vertices. Multiple initial sulcus seed regions are determined based on multiple adjacent sulcus seed points, and multiple initial gyral seed regions are determined based on multiple adjacent gyral seed points. Second, the connectivity between the initial sulcus seed regions and the connectivity between the initial gyral seed regions are determined respectively. Initial sulcus seed regions with connectivity are merged to obtain multiple merged sulcus seed regions, and initial gyral seed regions with connectivity are merged to obtain multiple merged gyral seed regions. Initial gyral seed regions with non-connectivity are... Each sulcus seed initial region is determined as an independent sulcus seed region, and each gyrus seed initial region with non-connectivity is determined as an independent gyrus seed region. Then, all vertices in sulcus seed merging regions, gyrus seed merging regions, sulcus seed independent regions, and gyrus seed independent regions with fewer than a preset number of vertices, as well as all remaining vertices, are determined as transition vertices, resulting in an initial transition vertex set. Sulcus seed merging regions, gyrus seed merging regions, sulcus seed independent regions, and gyrus seed independent regions with more than or equal to a preset number of vertices are determined as initial inclusion regions. Finally, based on each initial inclusion region, each transition vertex in the initial transition vertex set is iteratively included to obtain multiple sulcus seed target regions and gyrus seed target regions, thereby obtaining a personalized brain partitioning map and realizing brain partitioning. This application uses objective three-dimensional mesh data of the brain surface to determine three-dimensional geometric information, i.e., shape index, and further automates partitioning, avoiding the subjectivity of manual partitioning and improving the accuracy of brain partitioning. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A schematic flowchart of an individualized partitioning method based on brain surface geometric features provided in an embodiment of this application;

[0053] Figure 2 This is a schematic diagram of three-dimensional mesh data;

[0054] Figure 3 This is a schematic diagram of the regional optimization process;

[0055] Figure 4This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] The purpose of this application is to provide a method, device, medium, and product for individualized brain partitioning based on brain surface geometric features, with the aim of improving the accuracy of brain partitioning.

[0058] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] In one exemplary embodiment, such as Figure 1 As shown, a personalized partitioning method based on brain surface geometric features is provided, including:

[0060] Step 01: Obtain three-dimensional mesh data of the brain surface; the three-dimensional mesh data includes multiple vertices and multiple edges connecting two adjacent vertices.

[0061] Specifically, the three-dimensional mesh data of the brain surface is obtained from magnetic resonance imaging (MRI) data. MRI can utilize various equipment and models, such as 1.5T and 3T, used in clinical or research applications. During MRI acquisition, the standard 1mm slice thickness T1-weighted imaging parameters are used. The acquired T1-weighted imaging data can be stored in various formats and can be used to generate three-dimensional mesh data using various mature software, including but not limited to Freesurfer and Brainsuite. The final format of the three-dimensional mesh data is .ply.

[0062] like Figure 2 As shown, the adjacency relationship of a vertex in 3D mesh data refers to the information of all adjacent vertices contained in that vertex. Each vertex in 3D mesh data has a unique label and corresponding spatial coordinates. Two vertices are considered adjacent when they touch to form a line segment (i.e., an edge). Figure 2 In the diagram, solid lines represent connections within the same layer, and dashed lines represent connections between layers. Vertex V3 is adjacent to vertices V1, V2, and V6, meaning that the adjacent vertices of vertex V3 include vertices V1, V2, and V6. This adjacency relationship is mainly used for calculating Gaussian curvature and mean curvature, as well as for vertex processing when generating personalized partition maps.

[0063] Step 02: Determine the shape index of each vertex based on the 3D mesh data.

[0064] As an optional implementation, step 02 includes:

[0065] Step 021: Determine the curvature parameters of each vertex based on the 3D mesh data; the curvature parameters include: Gaussian curvature and mean curvature.

[0066] As an optional implementation, step 021 includes:

[0067] Step 0211: Determine any vertex as the target vertex.

[0068] Step 0212: Using the bending parameter calculation formula, calculate the bending parameters of the target vertex based on the 3D mesh data; the bending parameter calculation formula includes:

[0069]

[0070] Where K is the Gaussian curvature of the target vertex; S is the total area of ​​the region formed by the target vertex and all adjacent vertices; N is the number of adjacent vertices of the target vertex; θ i Let be the vertex angle of the triangle formed by the target vertex, the i-th adjacent vertex, and the (i+1)-th adjacent vertex. When i = N, the (i+1)-th adjacent vertex is the first adjacent vertex. i Let l be the length of the edge formed by the target vertex and the i-th adjacent vertex; i+1 k is the length of the edge formed by the target vertex and its (i+1)th adjacent vertex. i Let be the length of the edge formed by the i-th adjacent vertex and the (i+1)-th adjacent vertex of the target vertex; H is the average curvature of the target vertex; β i Let be the dihedral angle formed by the target vertex, the i-th adjacent vertex, and the (i+1)-th adjacent vertex.

[0071] Specifically, all adjacent vertices of each target vertex are numbered (from 1 to N) in advance, and all adjacent vertices of the target vertex are arranged in a ring around the target vertex. Therefore, adjacent vertex 1, adjacent vertex 2 and the target vertex form a triangle. Adjacent vertex 1, the last adjacent vertex (i.e., adjacent vertex N) and the target vertex form another triangle. Thus, when i = N, the (i+1)th adjacent vertex is the first adjacent vertex.

[0072] Step 022: Determine the maximum principal vector and minimum principal vector of each vertex based on the curvature parameters of each vertex.

[0073] As an optional implementation, step 022 includes:

[0074] Using the principal vector calculation formula, the maximum and minimum principal vectors of each vertex are calculated based on the curvature parameters of each vertex. The principal vector calculation formula includes:

[0075]

[0076] Where, k max k is the maximum principal vector; min It is the smallest principal vector.

[0077] Step 023: Determine the shape index of each vertex based on the maximum and minimum principal vectors of each vertex.

[0078] As an optional implementation, step 023 includes:

[0079] Using the shape index calculation formula, the shape index of each vertex is calculated based on its maximum and minimum principal vectors. The shape index calculation formula includes:

[0080]

[0081] SI stands for shape index.

[0082] Step 03: Based on the shape index of each vertex, divide each vertex into sulcus seed points, gyri seed points, and remaining vertices.

[0083] As an optional implementation, step 03 includes:

[0084] Step 031: Determine any vertex as the current vertex.

[0085] Step 032: When the shape index of the current vertex is less than -0.5, the current vertex is determined as a brain sulcus seed point.

[0086] Step 033: When the shape index of the current vertex is greater than 0.5, the current vertex is determined as the gyri seed point.

[0087] Step 034: When the shape index of the current vertex is between -0.5 and 0.5, the current vertex is determined as the remaining vertex.

[0088] Step 04: Determine multiple initial regions of brain sulcus seeds based on multiple adjacent brain sulcus seed points, and determine multiple initial regions of brain gyri seeds based on multiple adjacent brain gyri seed points.

[0089] Specifically, when the SI of a vertex is greater than 0.5, it is marked as a gyrus. Since gyri are scattered throughout the brain, and each gyrus has an area (composed of multiple adjacent vertices), these adjacent vertices need to be grouped together to form gyral seed points. Similarly, when the SI of a vertex is less than -0.5, it is marked as a sulcus. Since sulci are scattered throughout the brain, and each sulcus has an area (composed of multiple adjacent vertices), these adjacent vertices need to be grouped together to form sulcus seed points. In fact, there are transitional regions (SI between -0.5 and 0.5) between the strict gyri and sulci. Therefore, it is necessary to use the sulcus and gyri seed points as starting points to incorporate these transitional regions into the sulcus and gyri ranges, ultimately forming a continuous, personalized brain region map.

[0090] Step 05: Determine the connectivity between the initial seed regions of each sulcus and the initial seed regions of each gyrus; connectivity is either connected or disconnected; connectivity includes adjacent and intersecting.

[0091] Step 06: Merge the initial regions of the brain sulci that are connected to obtain multiple merged brain sulci regions, and merge the initial regions of the brain gyri that are connected to obtain multiple merged brain gyri regions.

[0092] Step 07: Determine the initial regions of each sulcus seed that are not connected as independent sulcus seed regions, and determine the initial regions of each gyrus seed that are not connected as independent gyrus seed regions.

[0093] Step 08: Determine all vertices in the sulcus seed merging region, gyrus seed merging region, sulcus seed independent region, and gyrus seed independent region with a vertex count less than the preset number, as well as all remaining vertices, as transition vertices to obtain the initial transition vertex set.

[0094] Specifically, because the undulations on the brain surface (i.e., sulci and gyri) vary in size (i.e., the shape fluctuations of sulci and gyri have different scales), very small fluctuations (i.e., the number of initial sulcus and gyri seed regions with fewer than a preset number of vertices, the preset number being 10) may be noise, invisible to the naked eye, or clinically insignificant. Therefore, all vertices in the merged sulcus and gyri seed regions containing fewer than 10 vertices are dissolved and merged into a transition region, i.e., updated as transition vertices.

[0095] Step 09: Determine the sulcus seed merging region, gyrus seed merging region, sulcus seed independent region, and gyrus seed independent region with a vertex number greater than and / or equal to the preset number as the initial inclusion region.

[0096] Step 10: Based on each initial inclusion region, iteratively include each transition vertex in the initial transition vertex set to obtain multiple brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partition map and realizing brain partitioning.

[0097] As an optional implementation, step 10 includes:

[0098] Step 101: Based on each initial inclusion region, all transition vertices in the initial transition vertex set are iteratively included multiple times to obtain individual brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partition map and realizing brain partitioning; wherein, the inclusion process at any current iteration number includes:

[0099] Step 1011: Determine whether each transition vertex in the transition vertex set before the update under the current iteration number has an adjacency relationship with any vertex in the included region before the update under the current iteration number; when the current iteration number is the initial iteration number, the transition vertex set before the update under the current iteration number is the initial transition vertex set, and the included region before the update under the current iteration number is the initial included region; when the current iteration number is not the initial iteration number, the transition vertex set before the update under the current iteration number is the updated transition vertex set under the previous iteration number, and the included region before the update under the current iteration number is the updated included region under the previous iteration number; the included region includes: the sulcus seed included region and the gyrus seed included region.

[0100] Step 1012: Include all transition vertices with adjacency relationships into the pre-update inclusion region at the current iteration number, thus obtaining the updated inclusion region at the current iteration number.

[0101] Step 1013: Remove all transition vertices with adjacency from the set of transition vertices before the update in the current iteration number, and retain all transition vertices without adjacency in the set of transition vertices before the update in the current iteration number, to obtain the updated set of transition vertices in the current iteration number.

[0102] Step 1014: Determine whether the number of transition vertices in the updated transition vertex set at the current iteration number is 0.

[0103] Step 1015: If not, determine the updated transition vertex set under the current iteration number as the transition vertex set before the update under the next iteration number, determine the updated inclusion region under the current iteration number as the inclusion region before the update under the next iteration number, update the current iteration number to the next iteration number, and return "determine whether each transition vertex in the transition vertex set before the update under the current iteration number has an adjacency relationship with any vertex in the inclusion region before the update under the current iteration number".

[0104] Step 1016: If yes, then the updated regions for each sulcus seed inclusion and each region for each gyrus seed inclusion under the current iteration number are respectively determined as the target regions for sulcus seeds and gyrus seeds, thereby obtaining a personalized brain partition map and realizing the partitioning of the brain.

[0105] Specifically, the process of region optimization in steps 05-10 is as follows: Figure 3 As shown.

[0106] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement a personalized partitioning method based on brain surface geometry features.

[0107] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements a personalized partitioning method based on brain surface geometry.

[0108] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements an individualized partitioning method based on brain surface geometry.

[0109] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a personalized partitioning method based on brain surface geometry features.

[0110] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0112] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A personalized partitioning method based on brain surface geometric features, characterized in that, The individualized partitioning method based on brain surface geometry features includes: Acquire three-dimensional mesh data of the brain surface; the three-dimensional mesh data includes multiple vertices and multiple edges connecting two adjacent vertices; Based on the three-dimensional mesh data, determine the shape index of each vertex; Based on the shape index of each vertex, each vertex is divided into sulcus seed points, gyri seed points and remaining vertices; Multiple initial regions of brain sulcus seeding are determined based on multiple adjacent brain sulcus seeding points, and multiple initial regions of brain gyri seeding are determined based on multiple adjacent brain gyri seeding points. The connectivity between the initial seed regions of each sulcus and the connectivity between the initial seed regions of each gyrus are determined respectively; the connectivity is either connected or disconnected; connectivity includes adjacent and intersecting. The initial regions of the brain sulci with connectivity are merged to obtain multiple merged brain sulci regions, and the initial regions of the brain gyri with connectivity are merged to obtain multiple merged brain gyri regions. Each sulcus seed initial region with non-connectivity is defined as an independent sulcus seed region, and each gyrus seed initial region with non-connectivity is defined as an independent gyrus seed region. All vertices in the sulcus seed merging region, gyrus seed merging region, sulcus seed independent region and gyrus seed independent region with fewer vertices than the preset number, as well as all remaining vertices, are determined as transition vertices to obtain the initial transition vertex set. The sulcus seed merging region, gyrus seed merging region, sulcus seed independent region and gyrus seed independent region with a vertex number greater than and / or equal to the preset number are all determined as the initial inclusion region. Based on each initial inclusion region, each transition vertex in the initial transition vertex set is iteratively included to obtain multiple brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partition map and realizing brain partitioning; Based on each initial inclusion region, the transition vertices in the initial transition vertex set are iteratively included to obtain multiple sulcus seed target regions and gyral seed target regions, thereby obtaining a personalized brain partitioning atlas, realizing the partitioning of the brain, including: Based on each initial inclusion region, all transition vertices in the initial transition vertex set are iteratively included multiple times to obtain multiple brain sulcus seed target regions and brain gyri seed target regions, thereby obtaining a personalized brain partitioning map and realizing brain partitioning; wherein, the inclusion process at any current iteration number includes: Determine whether each transition vertex in the pre-update transition vertex set under the current iteration number has an adjacency relationship with any vertex in the pre-update inclusion region under the current iteration number. When the current iteration number is the initial iteration number, the pre-update transition vertex set under the current iteration number is the initial transition vertex set, and the pre-update inclusion region under the current iteration number is the initial inclusion region. When the current iteration number is not the initial iteration number, the pre-update transition vertex set under the current iteration number is the updated transition vertex set under the previous iteration number, and the pre-update inclusion region under the current iteration number is the updated inclusion region under the previous iteration number. The inclusion region includes: the sulcus seed inclusion region and the gyrus seed inclusion region. Each transition vertex with an adjacency relationship is included in the pre-update inclusion region at the current iteration number, resulting in the updated inclusion region at the current iteration number. Remove all transition vertices that are adjacent from the set of transition vertices before the update in the current iteration number, and retain all transition vertices that are not adjacent in the set of transition vertices before the update in the current iteration number, to obtain the set of transition vertices after the update in the current iteration number. Determine if the number of transition vertices in the updated transition vertex set at the current iteration number is 0; If not, then determine the updated transition vertex set under the current iteration number as the transition vertex set before the update under the next iteration number, determine the updated inclusion region under the current iteration number as the inclusion region before the update under the next iteration number, update the current iteration number to the next iteration number, and return "determine whether each transition vertex in the transition vertex set before the update under the current iteration number has an adjacency relationship with any vertex in the inclusion region before the update under the current iteration number". If so, the updated regions for each sulcus seed inclusion and each region for each gyrus seed inclusion under the current iteration number are determined as the target regions for the sulcus seed and the target regions for the gyrus seed, respectively, thereby obtaining a personalized brain partition map and realizing the partitioning of the brain.

2. The individualized partitioning method based on brain surface geometric features according to claim 1, characterized in that, Based on the aforementioned 3D mesh data, the shape index of each vertex is determined, including: Based on the three-dimensional mesh data, the curvature parameters of each vertex are determined; the curvature parameters include: Gaussian curvature and mean curvature; Based on the curvature parameters of each vertex, determine the maximum principal vector and minimum principal vector of each vertex. The shape index of each vertex is determined based on the maximum and minimum principal vectors of each vertex.

3. The individualized partitioning method based on brain surface geometric features according to claim 2, characterized in that, Based on the three-dimensional mesh data, the bending parameters of each vertex are determined, including: Define any vertex as the target vertex; Using the bending parameter calculation formula, the bending parameters of the target vertex are calculated based on the three-dimensional mesh data; the bending parameter calculation formula includes: ； ； ； in, The Gaussian curvature of the target vertex; The total area of ​​the region formed by the target vertex and all its adjacent vertices; The number of adjacent vertices of the target vertex; Let be the vertex angle of the triangle formed by the target vertex, the i-th adjacent vertex, and the (i+1)-th adjacent vertex. When i=N, the (i+1)-th adjacent vertex is the 1-th adjacent vertex. Let be the length of the edge formed by the target vertex and the i-th adjacent vertex; Let be the length of the edge formed by the target vertex and its (i+1)th adjacent vertex; Let be the length of the edge formed by the i-th adjacent vertex and the (i+1)-th adjacent vertex of the target vertex; The average curvature of the target vertex; Let be the dihedral angle formed by the target vertex, the i-th adjacent vertex, and the (i+1)-th adjacent vertex.

4. The individualized partitioning method based on brain surface geometric features according to claim 3, characterized in that, Based on the curvature parameters of each vertex, determine the maximum and minimum principal vectors of each vertex, including: Using the principal vector calculation formula, the maximum and minimum principal vectors of each vertex are calculated based on the curvature parameters of each vertex; the principal vector calculation formula includes: ； ； in, It is the largest principal vector; It is the smallest principal vector.

5. The individualized partitioning method based on brain surface geometric features according to claim 4, characterized in that, Determine the shape index of each vertex based on its maximum and minimum principal vectors, including: Using the shape index calculation formula, the shape index of each vertex is calculated based on the maximum and minimum principal vectors of each vertex; the shape index calculation formula includes: ； in, This is the shape index.

6. The individualized partitioning method based on brain surface geometric features according to claim 1, characterized in that, Based on the shape index of each vertex, each vertex is divided into sulcus seed points, gyral seed points, and remaining vertices, including: Define any vertex as the current vertex; When the shape index of the current vertex is less than -0.5, the current vertex is determined as a brain sulcus seed point; When the shape index of the current vertex is greater than 0.5, the current vertex is determined as a gyri seed point; When the shape index of the current vertex is between -0.5 and 0.5, the current vertex is determined as the remaining vertex.

7. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the individualized partitioning method based on brain surface geometry as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the individualized partitioning method based on brain surface geometry as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the individualized partitioning method based on brain surface geometry as described in any one of claims 1-6.

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