公开(公告)号：US20250078279A1

公开(公告)日：2025-03-06

申请号：US18279316

申请日：2022-12-29

Applicant: SHANDONG UNIVERSITY

Inventor： Zhi LIU ,  Mujun AN ,  Kemeng TAO ,  Yankun CAO

IPC: G06T7/10 ,  G06T7/33 ,  G06T17/20

Abstract: The present disclosure provides a method and a system for fully automatically segmenting a cerebral cortex surface based on a graph network, belongs to the field of medical image technology and devices, and solves the problem that space mapping noise is easily introduced in the mapping process in the related art, and a lot of time is cost for mapping to a sphere surface space to finish segmentation and then mapping back to an original space. The method includes: registering a cerebral magnetic resonance image to a standard template space; performing cerebral cortex surface reconstruction on the registered cerebral magnetic resonance image based on a deep neural network; calculating adjacency matrices among the vertexes of the reconstructed cerebral cortex; acquiring corresponding distinguishing features of each grid vertex of the cerebral cortex surface as corresponding feature vectors of the point so as to obtain a cerebral cortex surface segmentation result; and mapping the cerebral cortex surface segmentation result back to the original coordinate space from the standard template space. According to the present disclosure, graph structure modeling is performed on the reconstructed cerebral cortex surface, and the global topological structure features are learned based on the graph network, thus realizing the accurate segmentation of the cerebral cortex surface.