ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection

In clinical medicine, precise image segmentation can provide substantial support to clinicians. However, achieving such precision often requires a large amount of finely annotated data, which can be costly. Scribble annotation presents a more efficient alternative, boosting labeling efficiency. However, utilizing such minimal supervision for medical image segmentation training, especially with scribble annotations, poses significant challenges. To address these challenges, we introduce ScribbleVS, a novel framework that leverages scribble annotations. We introduce a Regional Pseudo Labels Diffusion Module to expand the scope of supervision and reduce the impact of noise present in pseudo labels. Additionally, we propose a Dynamic Competitive Selection module for enhanced refinement in selecting pseudo labels. Experiments conducted on the ACDC and MSCMRseg datasets have demonstrated promising results, achieving performance levels that even exceed those of fully supervised methodologies. The codes of this study are available at https://github.com/ortonwang/ScribbleVS.