Scribble-Supervised Medical Image Segmentation Using Contour Mixed Pseudo Labels Supervision and Contour Auxiliary Labels Supervision

Scribble-supervised semantic segmentation has emerged as a promising alternative to fully supervised methods in medical imaging, owing to its low annotation cost and the inherent inclusion of contour information. However, the sparse supervision provided by scribble annotations, coupled with the underutilization of contour cues, often results in incomplete boundary representations in pseudo labels generated by the model. This limitation hinders the model’s ability to accurately capture complex anatomical structures, posing a significant challenge to achieving precise segmentation. To address this issue, we propose a novel three-branch network architecture. Built upon a multi-task learning framework, the model introduces a contour-assisted label supervision (contour auxiliary labels supervision) mechanism and a contour attention module to enhance the auxiliary decoder’s capability in extracting contour features. In addition, we design a contour mixed pseudo labels supervision strategy, which incorporates contour-enhanced representations into the pseudo-label generation process, thereby providing more informative and higher-quality supervision for scribble-based learning. We evaluate our method on the ACDC, MSCMR, and SegPC-2021 datasets. Experimental results demonstrate that our approach consistently outperforms state-of-the-art methods in terms of accuracy, robustness, and generalization. The scribble annotations and experimental code for the SegPC-2021 dataset are available at Github.