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The study aims to develop a framework for zero-shot medical image segmentation using advanced vision models.

March 29, 2026PLoS ONEOpen Access

MedZeroSeg: Zero-shot medical image segmentation via vision foundation models

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The study aims to develop a framework for zero-shot medical image segmentation using advanced vision models.
Introduced MedZeroSeg for medical image segmentation challenges.
Utilized CLIP and SAM for enhanced zero-shot capabilities.
Implemented a Dual-Path Feature Extraction Module for detailed and contextual understanding.
Developed a Context-Enhanced Hard-Negative Contrast Loss for improved contrastive learning.
Conducted experiments on cardiac MRI, multi-organ abdominal CT, and chest X-ray datasets.
MedZeroSeg demonstrated superior performance in zero-shot segmentation tasks.
Achieved strong generalization across different medical imaging modalities.
Showed minimal dependency on large annotated datasets.

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Novel framework achieves precise zero-shot segmentation in medical images, reducing data requirements.

Zhang et al. (2026) studied this question.

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