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Research on Medical Image Segmentation Based on SAM and Its Future Prospects
Kangxu Fan1, Liang Liang1, Hao Li1
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioengineering (Basel, Switzerland)
|June 26, 2025
Summary
The Segment Anything Model (SAM) shows promise for medical image segmentation but requires adaptation. Research explores refining SAM for medical imaging challenges, guiding future foundational model development.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Computer Vision
Background:
- Prompt-based models, including the Segment Anything Model (SAM), have significantly advanced image segmentation.
- SAM's versatility is notable, but its direct application to medical images is uncertain due to domain-specific challenges.
Purpose of the Study:
- To analyze current adaptations of SAM for medical image segmentation.
- To benchmark SAM's performance and identify methodological refinements for medical imaging.
Main Methods:
- Empirical benchmarking of SAM on medical image datasets.
- Methodological analysis of strategies to bridge the gap between SAM and medical imaging requirements.
Main Results:
- Direct application of SAM to complex medical datasets may not yield optimal results currently.
- Adaptation efforts provide valuable insights for tailoring foundational models to medical image analysis.
Conclusions:
- SAM has considerable potential for medical image segmentation despite current limitations.
- Future research should focus on developing specialized foundational models for intricate medical imaging tasks.
