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Practical application of SAM for breast nodules segmentation.

Wei Fan1, Ansheng Li1, Mingze Xu2

  • 1Department of Radiology, Rocket Force Characteristic Medical Center of the Chinese People's Liberation Army, Beijing, China.

Frontiers in Oncology
|March 26, 2026
PubMed
Summary

This study explored using the Segment Anything Model (SAM) for breast nodule segmentation. Utilizing MedSAM initial weights and fixed prompt boxes significantly improved segmentation accuracy for early breast cancer detection.

Keywords:
DCESAMbreast cancerbreast nodules segmentationmagnetic resonance imaging

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Computer-Aided Diagnosis

Background:

  • Breast cancer is a leading cause of death globally, making early detection crucial.
  • Accurate breast nodule segmentation is vital for timely diagnosis and treatment.
  • Training medical image segmentation models is challenging due to limited annotated data.

Purpose of the Study:

  • To investigate the effectiveness of the Segment Anything Model (SAM) for breast nodule segmentation.
  • To explore the impact of initial weights, organ masks, and prompt boxes on SAM's performance in medical imaging.
  • To assess the feasibility of applying SAM for practical breast cancer detection.

Main Methods:

  • Evaluated SAM's performance on breast nodule segmentation using various configurations.
  • Investigated the influence of initial weights, specifically MedSAM.
  • Assessed the utility of organ (breast) masks and different prompt box strategies.

Main Results:

  • The use of MedSAM initial weights demonstrated superior segmentation results compared to standard SAM.
  • Employing single, fixed prompt boxes yielded optimal segmentation outcomes.
  • The proposed approach showed promise for practical application in breast cancer screening.

Conclusions:

  • MedSAM initial weights and fixed prompt boxes enhance breast nodule segmentation accuracy.
  • SAM shows potential for improving early breast cancer detection and diagnosis.
  • Further research can optimize SAM for clinical integration in breast imaging.