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CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

Eytan Kats, Christoph Grossbroehmer, Ziad Al-Haj Hemidi, Fenja Falta, Wiebke Heyer, Mattias P. Heinrich

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.

CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

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.
Paper Structure (12 sections, 4 equations, 3 figures, 3 tables)

This paper contains 12 sections, 4 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Problem definition
  4. Registration framework
  5. Equivariance constraint
  6. Joint optimization
  7. Experiments and results
  8. Datasets and evaluation metrics
  9. Implementation details
  10. Registration results
  11. Ablations study
  12. Discussion

Figures (3)

  • Figure S1: Comparison of hybrid registration methods. From left to right: (1) Feature extractor pretrained separately and used without further optimization during registration (SAMConvex li2023samconvex); (2) Feature extractor optimized exclusively with a registration loss during training (RegCyc bigalke2023unsupervised); (3) Proposed CoRe method, where the feature extractor is jointly optimized under both registration and contrastive loss objectives to enhance feature robustness and registration accuracy.
  • Figure S2: Overview of the proposed CoRe framework: The feature extractor is jointly optimized using registration and equivariance-based contrastive objectives, enabling robust and spatially coherent feature representations for precise displacement field estimation.
  • Figure S3: Qualitative results of the proposed CoRe method. From left to right: fixed image, fixed image with its segmentation overlay, fixed image with the overlay of the moving image segmentation, and fixed image with the overlay of the warped segmentation. The top two rows show examples from the AbdomenCT dataset in the axial plane, while the bottom two rows present examples from the RadChestCT dataset in axial and coronal planes.