Interpretable deformable image registration: A geometric deep learning perspective

• URL: http://arxiv.org/abs/2412.13294v2
• Date: Mon, 10 Mar 2025 12:42:18 GMT
• Title: Interpretable deformable image registration: A geometric deep learning perspective
• Authors: Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Huaqi Qiu, Julian McGinnis, Wenke Karbole, Martin Menten, Daniel Rueckert,
• Abstract summary: We present a theoretical foundation for designing an interpretable registration framework.<n>We formulate an end-to-end process that refines transformations in a coarse-to-fine fashion.<n>We conclude by showing significant improvement in performance metrics over state-of-the-art approaches.
• Score: 9.13809412085203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deformable image registration poses a challenging problem where, unlike most deep learning tasks, a complex relationship between multiple coordinate systems has to be considered. Although data-driven methods have shown promising capabilities to model complex non-linear transformations, existing works employ standard deep learning architectures assuming they are general black-box solvers. We argue that understanding how learned operations perform pattern-matching between the features in the source and target domains is the key to building robust, data-efficient, and interpretable architectures. We present a theoretical foundation for designing an interpretable registration framework: separated feature extraction and deformation modeling, dynamic receptive fields, and a data-driven deformation functions awareness of the relationship between both spatial domains. Based on this foundation, we formulate an end-to-end process that refines transformations in a coarse-to-fine fashion. Our architecture employs spatially continuous deformation modeling functions that use geometric deep-learning principles, therefore avoiding the problematic approach of resampling to a regular grid between successive refinements of the transformation. We perform a qualitative investigation to highlight interesting interpretability properties of our architecture. We conclude by showing significant improvement in performance metrics over state-of-the-art approaches for both mono- and multi-modal inter-subject brain registration, as well as the challenging task of longitudinal retinal intra-subject registration. We make our code publicly available

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