Contrastive Learning with Multi-Contrast Constraints for Segmentation in Renal Magnetic Resonance Imaging

Abstract

Motivation: Supervised deep learning provides state-of-the-art medical image segmentation when large labeled images are accessible. However, manual segmentation suffers from prolonged delineation. Goal(s): In response to the 2024 ISMRM Challenge “Repeat it With Me: Reproducibility Team Challenge”, we aim to show the effectiveness of contrastive learning to find suitable initialization for segmentation with limited annotation. Approach: We use a multi-contrast contrastive loss guided by representational constraints to learn discriminating features within multi-parametric renal MR images and fine-tune the pretrained model on segmentation tasks. Results: Our findings validate that pretraining diminishes the needed annotation effort by 60% for different imaging sequences and enhances segmentation performance. Impact: Multi-contrast contrastive learning reduces annotation effort to train deep-learning segmentation models, confirming prior findings in a new cohort, within the 2024 ISMRM Challenge “Repeat it With Me: Reproducibility Team Challenge” and indicating its potential to improve multi-parametric imaging workflows.