1MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China 2Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shangdong, China 3Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China
刊名
IEEE Journal of Biomedical and Health Informatics
年份
2025
页码
1-11
ISSN
2168-2194
摘要
A key challenge in registering pre- and post-operative brain tumor images lies in the anatomical inconsistencies caused by pathological changes and surgical resections. Recent efforts have addressed this issue by masking affected regions during optimization, but such approaches discard contextual information and rely on CNN backbones that implicitly model deformation, often overfitting to distant normal tissues and failing to capture the severe nonlinear distortions near the tumor. Correlation-b...更多
A key challenge in registering pre- and post-operative brain tumor images lies in the anatomical inconsistencies caused by pathological changes and surgical resections. Recent efforts have addressed this issue by masking affected regions during optimization, but such approaches discard contextual information and rely on CNN backbones that implicitly model deformation, often overfitting to distant normal tissues and failing to capture the severe nonlinear distortions near the tumor. Correlation-based alternatives enhance generalization to diverse deformation patterns by explicitly modeling geometric correspondences, yet they frequently yield unreliable matches in and around tumor regions, disrupting the deformation field. In this paper, we propose Cross-correlation Rectification-based Registration Network , the first framework that introduces an active rectification mechanism specifically for robustness and structurally coherent pre- to post-operative brain tumor image registration. Specifically, CRRNet achieves this through two complementary modules: 1) a Cross-correlation Analysis-Based Inconsistency module that identifies invalid correspondences via bidirectional loop-closure evaluation on cross-correlations, and 2) a Dual-level Correspondence Rectification module that adaptively integrates contextually reliable correlations from local and long-range perspectives to restore structurally coherent matches. This synergistic design retains the strengths of cross-correlation while effectively mitigating correspondence mismatches. Extensive experiments on multiple tumor benchmarks demonstrate the superiority of CRRNet. Specifically, on the BraTS-Reg dataset, it reduces the mean registration errors by 8.18% in near-tumor regions and 3.95% in far-from-tumor regions, surpassing state-of-the-art methods.收起
