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ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image

Research Medical/Healthcare AI

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Representative image for ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image

Merged summary

TL;DR - ESRVS segments retinal vessels using only one manually annotated image, propagating supervision with adapted DINOv3 features and refined pseudo-labels. It approaches fully supervised performance while substantially reducing expert annotation needs.

  • Selects a representative image for annotation and builds multi-granular vessel prototypes.
  • Combines prototype similarity with a physics-inspired prior, weighted pseudo-label training, and adversarial refinement.
  • Achieves the best Dice and clDice on six of eight datasets and the best HD95 on all eight among compared semi-supervised methods.
  • Retains 93.7% of fully supervised Dice and 95.1% of clDice on average with Mask2Former.

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ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image

arXiv cs.CV Mingzhi Xu, Yizhe Zhang 2026-07-27 arXiv:2607.24453
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-23 14:25:55.467552 UTC

TL;DR - ESRVS segments retinal vessels using only one manually annotated image, propagating supervision with adapted DINOv3 features and refined pseudo-labels. It approaches fully supervised performance while substantially reducing expert annotation needs.

  • Selects a representative image for annotation and builds multi-granular vessel prototypes.
  • Combines prototype similarity with a physics-inspired prior, weighted pseudo-label training, and adversarial refinement.
  • Achieves the best Dice and clDice on six of eight datasets and the best HD95 on all eight among compared semi-supervised methods.
  • Retains 93.7% of fully supervised Dice and 95.1% of clDice on average with Mask2Former.
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