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March 21, 2026EntropyOpen Access

PromptSeg: An End-to-End Universal Medical Image Segmentation Method via Visual Prompts

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Authors

MZMinfan ZhaoBWBingxun WangJSJun Shi

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Overview

Demonstrates improved segmentation in medical images, indicating enhanced generalization across diverse tasks.

Key Points

  • The research aims to develop a universal method for medical image segmentation using visual prompts.
  • Proposed PromptSeg, a transformer-based framework for 2D medical image segmentation.
  • Formulated segmentation as a conditional entropy minimization problem.
  • Leveraged visual prompts to improve task-specific semantic extraction.
  • Conducted experiments on CT and MRI datasets.
  • PromptSeg outperformed existing state-of-the-art segmentation methods.
  • Exhibited strong generalization capabilities across unseen datasets.
  • Required minimal annotated visual prompt pairs for effective segmentation.

Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/69be356f6e48c4981c673aa6https://doi.org/10.3390/e28030342
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Also Consider

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

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  5. 5Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation2024