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MD-SA2: optimizing Segment Anything 2 for multimodal, depth-aware brain tumor segmentation in sub-Saharan

Benjamin Li1, Kai Ding2, Dimah Dera3

  • 1Millburn High School, Millburn, New Jersey, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|April 25, 2025
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Summary

MD-SA2 significantly improves brain tumor segmentation on low-quality MRI scans. This machine learning approach enhances diagnostic accuracy, potentially reducing health disparities in underserved regions.

Keywords:
artificial intelligencebrain tumor segmentationmagnetic resonance imagingsegment anything

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Machine learning aids radiologists in medical image segmentation, but challenges remain with complex, multimodal, and variable-quality MRI scans for brain tumor segmentation.
  • Existing methods, including conventional machine learning and Segment Anything (SA)-based models, struggle with the intricacies of brain tumor imaging.

Purpose of the Study:

  • To address limitations in current brain tumor segmentation techniques for MRI.
  • To introduce MD-SA2, an adaptation of Segment Anything 2 (SA2) enhanced with a U-Net aggregator for improved medical image segmentation.

Main Methods:

  • Customized and fine-tuned SA2 for enhanced efficiency and accuracy.
  • Concatenated multi-modal MRI slices to improve tumor subtype delineation.
  • Integrated a lightweight U-Net aggregator with SA2 to incorporate depth awareness.
  • Evaluated performance on the 2023 BraTS-Africa dataset, comprising low-resolution MRI from sub-Saharan patients.

Main Results:

  • MD-SA2 achieved a tenfold statistically significant improvement over current methods, with a Dice coefficient of 0.7893.
  • Demonstrated superior Intersection over Union (IoU) and lower 95% Hausdorff distance metrics.
  • An ablation study confirmed the effectiveness of key MD-SA2 components.

Conclusions:

  • MD-SA2 shows strong potential for aiding brain tumor diagnosis and treatment planning.
  • The approach may help reduce health inequities, particularly in medically underserved areas facing data limitations.