弱监督回转铰对应的几何感知图匹配框架

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhibin He, Wuyang Li, Tianming Liu, Xiang Li, Junwei Han, Tuo Zhang, Yixuan Yuan
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引用次数: 0

摘要

实现脑内标记的精确对齐,如脑回铰(GH),将增强不同受试者脑功能的对应性,从而促进我们对脑解剖-功能关系和脑机制的理解。目前的方法主要集中在利用点对点接地真值来识别GHs的对应关系。然而,考虑到每个大脑中存在超过400个GH,在受试者之间标记整个大脑的点对点GH对应既费力又耗时。为了解决这个问题,我们提出了一个几何感知图匹配框架,称为GAGM,用于仅基于大脑先验信息的弱监督旋转铰链对应。具体来说,我们提出了一个形状感知图建立(SAGE)模块,以确保在GH中几何特征的全面表示。SAGE通过整合GH坐标、形状和GH之间的关系来构建一个结构化图,对整个大脑GH进行建模,并学习它们之间的空间关系。此外,为了降低优化难度,提出了区域感知图匹配(RAGM)模块用于多尺度匹配。RAGM利用GHs与大脑区域之间多尺度关系的先验知识,结合尺度间语义一致性,确保GH特征在区域内的一致性和区域间的可变性,最终实现准确的GH匹配。在HCP和CHCP两个公共数据集上进行的大量实验表明,我们的方法优于最先进的方法。我们的代码:https://github.com/ZhibinHe/GAGM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GAGM: Geometry-aware graph matching framework for weakly supervised gyral hinge correspondence
Achieving precise alignment of inter-subject brain landmarks, such as the gyral hinge (GH), would enhance the correspondence of brain function across subjects, thereby advancing our understanding of brain anatomy-function relationship and brain mechanisms. Recent methods mainly focus on identifying the correspondences of GHs by utilizing point-to-point ground truth. However, labeling point-to-point GH correspondences between subjects for the entire brain is laborious and time-consuming, given the presence of over 400 GHs per brain. To remedy this problem, we propose a Geometry-Aware Graph Matching framework, dubbed GAGM, for weakly supervised gyral hinge correspondence solely based on brain prior information. Specifically, we propose a Shape-Aware Graph Establishment (SAGE) module to ensure a comprehensive representation of geometry features in GH. SAGE constructs a structured graph by incorporating GH coordinates, shapes, and inter-GH relationships to model entire brain GHs and learns the spatial relation between them. Moreover, to reduce the optimization difficulties, Region-Aware Graph Matching (RAGM) module is proposed for multi-scale matching. RAGM leverages prior knowledge of the multi-scale relationship between GHs and brain regions and incorporates inter-scale semantic consistency to ensure both intra-region consistency and inter-region variability of GH features, ultimately achieving accurate GH matching. Extensive experiments on two public datasets, HCP and CHCP, demonstrate the superiority of our method over state-of-the-art methods. Our code: https://github.com/ZhibinHe/GAGM.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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