Learning Semi-Supervised Medical Image Segmentation from Spatial Registration

TL;DR

This paper introduces CCT-R, a novel semi-supervised medical image segmentation framework that leverages spatial registration information to improve segmentation accuracy with limited labeled data.

Contribution

CCT-R is the first to incorporate registration-based supervision and positive sampling into semi-supervised segmentation, enhancing contrastive learning with registration transforms.

Findings

CCT-R outperforms existing methods on two medical segmentation benchmarks.

Effective with as few as one labeled case.

Registration-based modules significantly improve segmentation accuracy.

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/ContrastiveCross-teachingWithRegistration.