mlsanityData to 14 Oct 2026

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

arXiv 2411.10237 ↗First posted 15 Nov 2024cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 15 Nov 2024.

Builds on

28 of 41 references.

References · 28

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16 more
  1. 2020BoxInst: High-Performance Instance Segmentation with Box Annotationscited by 151
  2. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  3. 2021Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversitycited by 128
  4. 2018On Regularized Losses for Weakly-supervised CNN Segmentationcited by 121
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  6. 2020Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistencycited by 102
  7. 2021Pointly-Supervised Instance Segmentationcited by 83
  8. 2022CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervisioncited by 64
  9. 2022Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervisioncited by 64
  10. 2020Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistencycited by 52
  11. 2018Learning to Segment Medical Images with Scribble-Supervision Alonecited by 48
  12. 2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47
  13. 2019Gated CRF Loss for Weakly Supervised Semantic Image Segmentationcited by 40
  14. 2020Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoderscited by 39
  15. 2023ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embeddingcited by 23
  16. 2020ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentationcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentationcited by 0
  2. 2026Bi-Level Collaborative Learning for Few-Shot Scribble-Supervised Medical Image Segmentationcited by 0