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Segment anything model for medical images?

Yuhao Huang1, Xin Yang1, Lian Liu1

  • 1National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.

Medical Image Analysis
|December 12, 2023
PubMed
Summary

The Segment Anything Model (SAM) shows promise for medical image segmentation but requires careful prompting and fine-tuning for optimal performance across diverse cases. Its utility in assisting human annotation is also highlighted.

Keywords:
Medical image segmentationMedical object perceptionSegment anything model

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Area of Science:

  • Computer Vision
  • Medical Imaging Analysis
  • Artificial Intelligence

Background:

  • The Segment Anything Model (SAM) is a foundational model for general image segmentation.
  • Medical image segmentation (MIS) presents unique challenges due to complex modalities, fine structures, and variable object scales.

Purpose of the Study:

  • To comprehensively evaluate SAM's performance on medical image segmentation tasks.
  • To establish a large-scale, diverse medical image segmentation dataset (COSMOS 1050K) for rigorous validation.
  • To analyze various SAM configurations and prompting strategies for MIS.

Main Methods:

  • Collected and organized 53 open-source datasets, creating the COSMOS 1050K dataset (18 modalities, 84 objects, 1050K images, 6033K masks).
  • Conducted extensive experiments analyzing SAM's performance with different versions (ViT-B, ViT-H) and prompting methods (Everything mode, box prompts, point prompts).
  • Evaluated SAM's potential for assisting human annotation and the impact of fine-tuning on specific medical tasks.

Main Results:

  • SAM demonstrated variable performance, excelling in some areas but showing instability in others.
  • SAM with ViT-H generally outperformed ViT-B, and box prompts were more effective than the Everything mode.
  • SAM showed potential for efficient human annotation and performance gains after task-specific fine-tuning (4.39%-6.68% DICE improvement).

Conclusions:

  • SAM is a powerful tool for medical image segmentation but requires careful prompt engineering and fine-tuning for robust application.
  • The COSMOS 1050K dataset provides a valuable resource for future research in medical image segmentation with foundation models.
  • Further research is needed to address SAM's sensitivity to prompt randomness and optimize its use in complex medical imaging scenarios.