Dual-stream-based dense local features contrastive learning for semi-supervised medical image segmentation

Abstract

Most existing semi-supervised methods lack the ability to optimize feature distribution, which leads to poor inter-class separability and suboptimal decision boundaries during prediction. Additionally, the contours of targets in medical images are often ambiguous, making it challenging to identify contour features and potentially leading to segmentation errors. To address these issues, we propose a novel semi-supervised segmentation framework called Dual-stream-based Dense Local Features Contrastive Learning. Specifically, our approach employs a prediction stream to generate a predicted segmentation map and Signed Distance Map, which mutually reinforce each other to enhance the exploitation of contour features. We also introduce a feature stream that incorporates a Dense Local Features Contrastive Learning module, consisting of Sampling by Shape Info (SSI) and Dense Local Features Contrast (DLFC). The SSI module utilizes local shape information to create a Balanced Coefficient, which guides the sampling of positive and negative pairs between dense local features. The DLFC module effectively enhances intra-class compactness and inter-class separability by utilizing contrastive learning and we additionally introduce consistency regularization to promote the efficiency of contrastive learning. Extensive experiments on medical image segmentation benchmark datasets demonstrate our approach achieves significant improvements against existing state-of-the-art methods.