SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms.

Journal: Medical physics
Published Date: Oct 1, 2021

Abstract

PURPOSE: Measurements of breast arterial calcifications (BAC) can offer a personalized, non-invasive approach to risk-stratify women for cardiovascular diseases such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications.

Authors

  • Xiaoyuan Guo
    Department of Computer Science, Emory University, Decatur, Georgia, USA.
  • W Charles O'Neill
    School of Medicine, Emory University, Decatur, Georgia, USA.
  • Brianna Vey
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Tianen Christopher Yang
    School of Medicine, Emory University, Decatur, Georgia, USA.
  • Thomas J Kim
    College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA.
  • Maryzeh Ghassemi
    Department of Computer Science/Medicine, Toronto University, Toronto, Canada.
  • Ian Pan
    Warren Alpert Medical School, Brown University, Providence, RI.
  • Judy Wawira Gichoya
    Department of Interventional Radiology, Oregon Health & Science University, Portland, Oregon; Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia.
  • Hari Trivedi
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.

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