A GPU neuron skeleton extraction method based on topology pruning backbone growth

Abstract

The application discloses a GPU neuron skeleton extraction method based on topological simplified trunk growth, relates to the technical field of neural information science and three-dimensional biomedical image processing, and comprises the following steps: carrying out threshold filtering and denoising processing on input volume data, and reserving the maximum connected domain based on three-dimensional connected component labeling to remove isolated noise; under the condition of given cell center coordinates, the cell region is determined through spherical expansion and removed from the foreground to highlight the neural protrusion structure such as dendrite and axon; after obtaining the rough skeleton, topological simplification is realized by using a GPU, a path score function combining path length and average normalized brightness is constructed, the trunk path with the optimal score is selected and reserved through iteration, the trunk continuity is enhanced, and the false branch caused by noise and fracture is inhibited; and the application can realize robust skeleton extraction in three-dimensional data with noise interference and uneven brightness, and is suitable for neuron morphology reconstruction and quantitative analysis and the like.

Claims

1. A method for extracting the backbone of a GPU neuron based on topologically simplified trunk growth, characterized in that, The method, applied to three-dimensional neuronal microscopic imaging data, includes: S1, Data preprocessing: Threshold filtering and denoising are performed on the volume data to obtain a foreground voxel set, and three-dimensional connected component labeling is performed on the foreground voxel set to retain the largest connected component to remove isolated noise. S2, Data Acquisition: Under the condition of obtaining the coordinates of the cell body center, a spherical expansion is performed starting from the cell body center to determine the cell body region, and the cell body region is removed from the foreground voxel set to obtain the neural protrusion region; S3, Topology simplification: A coarse skeleton is generated based on the neural protrusion region, and the coarse skeleton is topology simplified on the GPU. S4, Stem Scoring and Growth: A path scoring function is constructed on the coarse skeleton. The path scoring function is at least related to the path length and the average normalized brightness of the voxels on the path. Then, the stem growth process is accelerated by GPU, and a hierarchical stem retention strategy of "coarse to fine, stem priority" is adopted: First, the longest global path is determined as the stem through two breadth-first searches. Then, based on the determined stem, the secondary branches with the best quality are iteratively discovered and retained. The longest path is used as the neuron stem and added to the retention set. The iterative process ends when any termination condition is met: the maximum length of a candidate path in a certain round is less than the global minimum length threshold, or the retention set covers the entire skeleton voxel point set, or the preset maximum number of iteration rounds is reached to prevent over-iteration, so as to obtain the target neuron skeleton.

2. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S1 includes the following steps: S21. Obtain cell body coordinates. The cell body center coordinates are obtained through manual annotation. In the interface for 3D neuron volume data, the operator records the corresponding position as the cell body center coordinates by specifying voxel indexes or spatial coordinates. This serves as the input parameter for subsequent cell body spherical expansion to determine the cell body region and perform knockout processing.

3. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S1 includes the following steps: S22, cell body coordinate removal, obtaining cell body center coordinates After that, with Starting with the foreground voxel set, a spherical expansion is performed to determine the cell body region, and this region is removed from the foreground. Let the foreground set after threshold segmentation and connected component filtering be... The candidate regions of the cell body are defined as follows: in To remove the radius from the cell body, when using iterative expansion, from Starting from the beginning, expand outwards layer by layer until the stopping condition is met. Then, cell body knockout is performed: in This is a foreground set of neural protrusions after cell body removal, used for subsequent rough skeleton generation, topology simplification, and trunk growth steps.

4. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S2 includes the following steps: S21, Threshold Filtering: Perform grayscale threshold filtering on the input three-dimensional neuron microscopic imaging data to initially separate the foreground and background of the neuron.

5. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S2 includes the following steps: S22, Connected region labeling, used to obtain a binary foreground mask after threshold filtering. Then, for all those that satisfy The voxels are labeled with 3D connected components. Under a preset 3D neighborhood connectivity criterion, the foreground voxel set is decomposed into several sets of non-overlapping connected components. , of which Both exist connected paths consisting of foreground voxels that allow them to reach each other; calculate the voxel count for each connected component. The connected component with the largest number of voxels is selected as the foreground component of the target neuron. Subsequently retained The remaining connected components are deleted, thereby removing isolated small connected components and random noise; the resulting foreground set is used for subsequent cell body removal and skeleton extraction steps.

6. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S3 includes the following steps: S31, Voxel neighborhood determination: During the topology simplification stage, for any candidate foreground voxel... First, based on the connectivity within its three-dimensional neighborhood, it is determined whether it satisfies the neighborhood constraint of a "deletable voxel". Two voxels in three-dimensional space... Neighborhood relations are determined by Manhattan distance Defined as: when It is 6-connected. It is 18-connected. For 26-connectivity, define accordingly neighborhood set Subsequently Within the neighborhood of "model" and "background", the connected components of "model" and "background" are judged respectively. Only when... A voxel is marked as removable only if it meets all three of the following core conditions: In its - Neighborhood There is one and only one inside. - Connected foreground components; In its - Neighborhood There is one and only one inside. - A connected background component, and this background component is connected to... for - Connected; At least one - Connected background neighbors, to adapt to GPU parallel judgment, further adopt... of The above neighborhood determination is achieved by template matching within the neighborhood: In any sub-iteration in either direction, if If the neighborhood matches the template corresponding to that direction, it is marked as deletable; The template can be represented by a Boolean equation as follows: in For neighboring voxels, Its logic is NOT. These represent the sets of neighbor indices in the template that are required to be "0", "1" and "at least one 1", respectively.

7. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S3 includes the following steps: S32 performs parallelization and memory access optimization on the GPU for "template matching / Boolean equation judgment of boundary voxels". First, a bit-encoding compression strategy is adopted: along... The shaft will be continuous The foreground or background state of a voxel is packaged into a 32-bit unsigned integer, allowing a single CUDA thread to process one package unit at a time, transforming the complex branching judgments per voxel into a single and efficient bitwise Boolean equation calculation.

8. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, In step S32 Its bit encoding can be expressed as: for a fixed With the beginning ,definition: in Foreground indicator function, The Bit correspondence Voxel state, followed by rapid acquisition and caching of neighborhood data: for Direction of neighbors, through Obtained by performing a displacement operation; for The directional neighbors are obtained by reading the adjacent packed integers, and the obtained neighborhood data is temporarily stored in the GPU's high-speed shared memory for subsequent template matching and unified calculation of Boolean equations.

9. The method for extracting the GPU neuron backbone based on topologically simplified backbone growth according to claim 1, characterized in that, Step S4 includes the following steps: S41, Core score calculation: After topological simplification to obtain the rough skeleton, the rough skeleton is considered as a connected structure composed of voxel points. Any two voxels are defined... and A path is formed by the sequence of connected voxels along the rough skeleton. ,in Indicates the first position on the path Individual points, in order to simultaneously characterize the topological dominance and imaging reliability of a path, for path Calculate the main score function: in The topological length of the path. The average normalized brightness of the path: in, Voxel representation Normalized brightness values ​​in the original image. For brightness weighting coefficients, when The scoring degenerates into a scoring based solely on topological length; when At that time, the brighter path will receive a higher score.

10. The method for extracting the backbone of a GPU neuron based on topologically simplified backbone growth according to claim 1, characterized in that, Step S4 includes the following steps: S42, GPU-accelerated optimization: During the trunk growth stage, the coarse skeleton is represented as a sparse graph structure with voxels as nodes. The cumulative calculation of path scores, the selection of optimal paths for candidate endpoints, and the trunk backtracking marking are parallelized on the GPU. The skeleton points near the cell body center are used as the root node set. Perform a hierarchical traversal of the skeleton graph and for each node Calculate the cumulative trunk score from the root to the node, and let the node brightness weight be: in This represents the normalized brightness of the node voxels in the original image. The brightness weight coefficient is then used to determine the distance from the root to the node. The cumulative score can be recursively derived as follows: in To satisfy the predecessor neighbor set of topological hierarchy constraints, and to synchronously record the predecessor pointer when the maximum value is obtained. This recursion can be executed in parallel on the GPU in units of "layers": Each node in the same layer The calculations are independent of each other and are suitable for being completed in parallel using thread blocks; Set of endpoints / leaf nodes The optimal backbone endpoint selection is achieved using parallel reduction computation: This yields the endpoint of the highest-rated main path, and then... pointer from Backtracking to the root set After marking the main path, nodes on that main path are added to the "retained main path set". In the next iteration, the remaining structure continues to branch or expand the main path. Node adjacency information is stored in a compact, compressed sparse row adjacency list format. Node brightness... With cumulative value The algorithm uses a structure array layout, and the maximum value search for endpoint scores combines intra-block shared memory reduction with atomic updates.

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