ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection
This work addresses the problem of reducing annotation costs for clinicians in medical imaging, though it is incremental as it builds on existing pseudo-labeling methods.
The paper tackles the challenge of training reliable medical image segmentation models using cost-effective scribble annotations instead of expensive pixel-level labels, achieving segmentation precision comparable to fully supervised models on datasets like ACDC, MSCMRseg, WORD, and BraTS2020.
In clinical medicine, precise image segmentation can provide substantial support to clinicians. However, obtaining high-quality segmentation typically demands extensive pixel-level annotations, which are labor-intensive and expensive. Scribble annotations offer a more cost-effective alternative by improving labeling efficiency. Nonetheless, using such sparse supervision for training reliable medical image segmentation models remains a significant challenge. Some studies employ pseudo-labeling to enhance supervision, but these methods are susceptible to noise interference. To address these challenges, we introduce ScribbleVS, a framework designed to learn from 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 introduce a Dynamic Competitive Selection module for enhanced refinement in selecting pseudo labels. Experiments conducted on the ACDC, MSCMRseg, WORD, and BraTS2020 datasets demonstrate promising results, achieving segmentation precision comparable to fully supervised models. The codes of this study are available at https://github.com/ortonwang/ScribbleVS.