Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.

Journal: Clinical imaging
PMID: 38101228

Abstract

PURPOSE: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD).

Authors

  • Emma K Schmidt
    Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
  • Chetana Krishnan
    Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
  • Ezinwanne Onuoha
    Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
  • Adriana V Gregory
    Department of Radiology, Mayo Clinic, Rochester, MN 55902, USA.
  • Timothy L Kline
    Department of Radiology, Mayo Clinic, Rochester, Minn.
  • Michal Mrug
    Division of Nephrology, University of Alabama and the Department of Veterans Affairs Medical Center, Birmingham, Alabama, USA.
  • Carlos Cardenas
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Harrison Kim
    Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA. Electronic address: Hyunkikim@uabmc.edu.

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