Boundary-Enhanced and Density-Guided Contrastive Learning for Semi/Weakly-Supervised Medical Image Segmentation

Medical image segmentation often requires large datasets with pixel-wise annotations. To alleviate this, we introduce BD-Net for semi-supervised and weakly supervised semantic segmentation (SWSS) using of sparse annotations (such as scribbles) alongside a few pixel-level annotations. BD-Net leverages a Boundary-Enhanced Module that improves boundary localization by extracting structural cues from annotated images through edge detection and multi-scale feature aggregation. Additionally, Density-Guided Contrastive Regularization enhances feature space compactness by using a contrastive loss informed by pixel density. Evaluation on the ACDC dataset shows BD-Net’s superior segmentation quality and generalizability compared to existing methods. The code will be available at https://github.com/Lemonzhoumeng/BD-Net.