Learning Semi-Supervised Medical Image Segmentation from Spatial Registration

Abstract

Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information--spatial registration transforms between image volumes. To address this we propose CCT-R a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs CCT-R incorporates two proposed modules: Registration Supervision Loss (RSL) and Registration-Enhanced Positive Sampling (REPS). The RSL leverages segmentation knowledge derived from transforms between labeled and unlabeled volume pairs providing an additional source of pseudo-labels. REPS enhances contrastive learning by identifying anatomically-corresponding positives across volumes using registration transforms. Experimental results on two challenging medical segmentation benchmarks demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings with as few as one labeled case. Our code is available at https://github.com/kathyliu579/ContrastiveCrossteachingWithRegistration.