CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

2026-03-24

AI Summary

Medical image registration is often hindered by intensity inconsistencies arising from modality differences and nonlinear tissue deformations, which compromise robustness. To address this challenge, this work proposes the first end-to-end integration of equivariant contrastive learning into a registration framework, jointly optimizing contrastive and registration objectives to learn deformation-invariant yet task-adaptive discriminative feature representations. By unifying self-supervised representation learning with non-rigid registration, the method significantly outperforms strong existing baselines on both inter- and intra-patient abdominal and thoracic image registration tasks, demonstrating its effectiveness and generalization capability.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to the robustness of registration methods. Recent approaches leveraging self-supervised representation learning show promise by pre-training feature extractors to generate robust anatomical embeddings, that farther used for the registration. In this work, we propose a novel framework that integrates equivariant contrastive learning directly into the registration model. Our approach leverages the power of contrastive learning to learn robust feature representations that are invariant to tissue deformations. By jointly optimizing the contrastive and registration objectives, we ensure that the learned representations are not only informative but also suitable for the registration task. We evaluate our method on abdominal and thoracic image registration tasks, including both intra-patient and inter-patient scenarios. Experimental results demonstrate that the integration of contrastive learning directly into the registration framework significantly improves performance, surpassing strong baseline methods.

Problem

• medical image registration
• intensity inconsistency
• nonlinear deformation
• robustness

Innovation

• contrastive learning
• medical image registration
• joint optimization
• equivariant representation
• self-supervised learning