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Related Experiment Video

Updated: Jun 25, 2025

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Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model.

Andrés Larroza1, Francisco Javier Pérez-Benito1, Raquel Tendero1

  • 1Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain.

Diagnostics (Basel, Switzerland)
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

The SAM-breast model accurately segments breast regions in mammograms, improving computer-aided diagnosis. This advanced segmentation method enhances breast delineation and pectoral muscle exclusion for better cancer detection.

Keywords:
breast segmentationmammographysegment anything model (SAM)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Mammography is vital for breast cancer screening but faces challenges like low contrast, noise, and artifacts.
  • Accurate breast segmentation is essential for effective Computer-Aided Diagnosis (CAD) systems in mammography.
  • Existing methods struggle with precise breast delineation and pectoral muscle exclusion in various mammogram views.

Purpose of the Study:

  • To introduce and evaluate the SAM-breast model for automated breast segmentation in mammograms.
  • To enhance the accuracy of breast region delineation and pectoral muscle exclusion.
  • To assess the model's performance across diverse datasets and mammogram views (MLO and CC).

Main Methods:

  • Adapted the Segment Anything Model (SAM) into the SAM-breast model for mammogram segmentation.
  • Trained the model on a large, multi-center proprietary dataset comprising 2492 mammograms.
  • Validated performance using independent test images from five datasets (two proprietary, three public).

Main Results:

  • Achieved a high overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) of 98.48% ± 2.10.
  • Demonstrated consistent performance across different datasets, vendors, and image resolutions.
  • Outperformed baseline and other deep learning-based segmentation methods.

Conclusions:

  • The SAM-breast model effectively segments the breast region in mammograms, showcasing SAM's adaptability.
  • The method provides robust, flexible, and generalizable breast segmentation capabilities.
  • This advancement holds significant potential for improving computer-aided diagnosis in breast cancer screening.