A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation

Abstract

Semi-supervised medical image segmentation models often face challenges such as empirical mismatch and data imbalance. Traditional methods, like the two-stream perturbation model, tend to over-rely on strong perturbation, leaving weak perturbation and labeled images underutilized. To overcome these challenges, we propose an innovative hybrid copy-paste (HCP) method within the strong perturbation branch, encouraging unlabeled images to learn more comprehensive semantic information from labeled images and narrowing the empirical distribution gap. Additionally, we integrate contrastive learning into the weak perturbation branch, where contrastive learning samples are selected through semantic grouping contrastive sampling (SGCS) to address memory and variance issues. This sampling strategy ensures more effective use of weak perturbation data. This approach is particularly advantageous for pixel segmentation tasks with severely limited labels. Finally, our approach is validated on the public ACDC (Automated Cardiac Diagnosis Challenge) dataset, achieving a 90.6% DICE score, with just 7% labeled data. These results demonstrate the effectiveness of our method in improving segmentation performance with limited labeled data.