articleElectronic ImagingJan 31, 2025BRONZE OA

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

RDRuining DengCCCan CuiQLQuan LiuTYTianyuan YaoLWLucas W. Remedios

Vanderbilt University · Vanderbilt University Medical Center · +3 more institutions

PubMed
Indexed incrossrefpubmed

Abstract

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results…

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Topics & keywords

Topics
Keywords
  • Digital pathology
  • Shot (pellet)
  • Segmentation
  • Computer science
  • Zero (linguistics)
  • Artificial intelligence
  • Computer vision
  • Computer graphics (images)
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