Adaptive Conditional Contrast-Agnostic Deformable Image Registration with Uncertainty Estimation

• URL: http://arxiv.org/abs/2601.05981v1
• Date: Fri, 09 Jan 2026 18:00:49 GMT
• Title: Adaptive Conditional Contrast-Agnostic Deformable Image Registration with Uncertainty Estimation
• Authors: Yinsong Wang, Xinzhe Luo, Siyi Du, Chen Qin,
• Abstract summary: We propose an adaptive conditional contrast-agnostic deformable image registration framework (AC-CAR) based on a random convolution-based contrast augmentation scheme.<n>AC-CAR can generalize to arbitrary imaging contrasts without observing them during training.<n>We enable our framework to provide contrast-agnostic registration uncertainty by integrating a variance network.
• Score: 10.687663039957336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable multi-contrast image registration is a challenging yet crucial task due to the complex, non-linear intensity relationships across different imaging contrasts. Conventional registration methods typically rely on iterative optimization of the deformation field, which is time-consuming. Although recent learning-based approaches enable fast and accurate registration during inference, their generalizability remains limited to the specific contrasts observed during training. In this work, we propose an adaptive conditional contrast-agnostic deformable image registration framework (AC-CAR) based on a random convolution-based contrast augmentation scheme. AC-CAR can generalize to arbitrary imaging contrasts without observing them during training. To encourage contrast-invariant feature learning, we propose an adaptive conditional feature modulator (ACFM) that adaptively modulates the features and the contrast-invariant latent regularization to enforce the consistency of the learned feature across different imaging contrasts. Additionally, we enable our framework to provide contrast-agnostic registration uncertainty by integrating a variance network that leverages the contrast-agnostic registration encoder to improve the trustworthiness and reliability of AC-CAR. Experimental results demonstrate that AC-CAR outperforms baseline methods in registration accuracy and exhibits superior generalization to unseen imaging contrasts. Code is available at https://github.com/Yinsong0510/AC-CAR.

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