🤖 AI Summary
Existing image registration methods rely on global smooth regularization, limiting their ability to model regionally heterogeneous deformations inherent in anatomical motion. To address this, we propose SegReg—a segmentation-driven medical image registration framework that leverages anatomical segmentation maps to guide region-adaptive deformation field estimation. SegReg decomposes global regularization into localized subregion optimizations and establishes, for the first time, an approximately linear relationship between registration accuracy and segmentation quality. Specifically, it employs a deep learning-based segmentation network to delineate anatomical subregions and computes local displacement fields in parallel within a unified registration backbone, subsequently fusing them into a globally consistent deformation field. Evaluated on multi-center cardiac, abdominal, and pulmonary datasets, SegReg achieves average improvements of 2–12% over state-of-the-art methods. With ground-truth segmentations, it attains a Dice score of 98.23% for critical structures, significantly enhancing anatomical consistency and registration robustness.
📝 Abstract
Deep learning has revolutionized image registration by its ability to handle diverse tasks while achieving significant speed advantages over conventional approaches. Current approaches, however, often employ globally uniform smoothness constraints that fail to accommodate the complex, regionally varying deformations characteristic of anatomical motion. To address this limitation, we propose SegReg, a Segmentation-driven Registration framework that implements anatomically adaptive regularization by exploiting region-specific deformation patterns. Our SegReg first decomposes input moving and fixed images into anatomically coherent subregions through segmentation. These localized domains are then processed by the same registration backbone to compute optimized partial deformation fields, which are subsequently integrated into a global deformation field. SegReg achieves near-perfect structural alignment (98.23% Dice on critical anatomies) using ground-truth segmentation, and outperforms existing methods by 2-12% across three clinical registration scenarios (cardiac, abdominal, and lung images) even with automatic segmentation. Our SegReg demonstrates a near-linear dependence of registration accuracy on segmentation quality, transforming the registration challenge into a segmentation problem. The source code will be released upon manuscript acceptance.