Collaborative Boundary Enhancement With Difficulty Awareness for Semi-Supervised Medical Image Segmentation

Abstract

Semi-supervised medical image segmentation aims to reduce the burden of manual annotation by leveraging a limited set of labeled data along with a large collection of unlabeled images, while maintaining reliable segmentation performance for clinical use. However, most existing methods neglect the consistency of boundary predictions across different learning views, often resulting in imprecise segmentation along object edges. In this paper, we propose a novel Collaborative Boundary enhancement with Difficulty Awareness (CoBDA) framework for semi-supervised medical image segmentation. Specifically, we propose a Consistency-aware Boundary Enhancement (CBE) module, which utilizes the agreement between predictions from two subnets to reinforce reliable edge signals and suppress ambiguous boundaries. Furthermore, to improve semantic coherence across the dual networks, we propose a Cross-prototype Semantic Alignment (CSA) strategy that facilitates mutual semantic representation alignment, thereby encouraging prediction consistency. Finally, we propose a Difficulty-aware Dynamic Weighting (DDW) strategy, which adaptively focuses the model’s attention on challenging regions. By integrating boundary cues and uncertainty, DDW ensures the learning process prioritizes hard-to-segment pixels. Extensive experiments on four benchmark datasets demonstrate that our model significantly outperforms state-of-the-art semi-supervised medical image segmentation methods. The code will be released upon acceptance at https://github.com/taozh2017/CoBDA