Medical image registration is a fundamental task for a wide range of clinical procedures. Automatic systems have been developed for image registration, where the majority of solutions are supervised techniques. However, those techniques rely on a large and well-representative corpus of ground truth, which is a strong assumption in the medical domain. To address this challenge, we propose a novel unified unsupervised framework for image registration and segmentation. The highlight of our framework is that patch-based representation is key for performance gain. We first propose a patch-based contrastive strategy that enforces locality conditions and richer feature representation. Secondly, we propose a patch stitching strategy to eliminate artifacts. We demonstrate, through our experiments, that our technique outperforms current state-of-the-art unsupervised techniques.
CITATION STYLE
Liu, L., Huang, Z., Liò, P., Schönlieb, C. B., & Aviles-Rivero, A. I. (2022). You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13386 LNCS, pp. 190–193). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-11203-4_21
Mendeley helps you to discover research relevant for your work.