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.

You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration / Liu, L.; Huang, Z.; Lio, P.; Schonlieb, C. -B.; Aviles-Rivero, A. I.. - 13386:(2022), pp. 190-193. (Intervento presentato al convegno 10th International Workshop on Biomedical Image Registration, WBIR 2020 tenutosi a Munich; deu) [10.1007/978-3-031-11203-4_21].

You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration

Lio P.;
2022

Abstract

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.
2022
10th International Workshop on Biomedical Image Registration, WBIR 2020
Image segmentation; Medical imaging
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration / Liu, L.; Huang, Z.; Lio, P.; Schonlieb, C. -B.; Aviles-Rivero, A. I.. - 13386:(2022), pp. 190-193. (Intervento presentato al convegno 10th International Workshop on Biomedical Image Registration, WBIR 2020 tenutosi a Munich; deu) [10.1007/978-3-031-11203-4_21].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1728005
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