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August 15, 2025ElectronicsOpen Access

Prototype-Guided Promptable Retinal Lesion Segmentation from Coarse Annotations

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Authors

QYQinji YuXDXiaowei Ding

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Overview

This approach demonstrates improved segmentation accuracy for retinal lesions using prototype learning and coarse annotations, indicating potential clinical applications.

Key Points

  • Prototype-based segmentation significantly enhances accuracy for retinal lesions, especially under coarse annotations.
  • Our model achieves superior performance over fully supervised methods with improved adaptability to variable lesions.
  • Using a U-Net backbone and a superpixel-guided weighting module minimizes background interference during segmentation.
  • This method emphasizes the effectiveness of prompt-based solutions in facilitating efficient medical image analysis.

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/68af4cd8ad7bf08b1ead6480https://doi.org/10.3390/electronics14163252
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1PrototypeGuided Meta Pixel Correction for 3D MultiLesion Segmentation With Partial Instance Annotations2026
  2. 2Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation2024
  3. 3Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts2026
  4. 4PromptSeg: An End-to-End Universal Medical Image Segmentation Method via Visual Prompts2026
  5. 5Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation2026