Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks

• URL: http://arxiv.org/abs/2311.08239v2
• Date: Mon, 4 Dec 2023 08:25:58 GMT
• Title: Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks
• Authors: Anna Reithmeir, Julia A. Schnabel, Veronika A. Zimmer
• Abstract summary: We propose to use a hypernetwork that learns the effect of the physical parameters of a physics-inspired regularizer on the resulting spatial deformation field. Our approach enables the efficient discovery of suitable, data-specific physical parameters at test time.
• Score: 3.776306113984256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image registration aims at identifying the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To date, the majority of the deep learning-based registration methods employ regularizers that enforce global spatial smoothness, e.g., the diffusion regularizer. However, such regularizers are not tailored to the data and might not be capable of reflecting the complex underlying deformation. In contrast, physics-inspired regularizers promote physically plausible deformations. One such regularizer is the linear elastic regularizer which models the deformation of elastic material. These regularizers are driven by parameters that define the material's physical properties. For biological tissue, a wide range of estimations of such parameters can be found in the literature and it remains an open challenge to identify suitable parameter values for successful registration. To overcome this problem and to incorporate physical properties into learning-based registration, we propose to use a hypernetwork that learns the effect of the physical parameters of a physics-inspired regularizer on the resulting spatial deformation field. In particular, we adapt the HyperMorph framework to learn the effect of the two elasticity parameters of the linear elastic regularizer. Our approach enables the efficient discovery of suitable, data-specific physical parameters at test time.

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