A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models.

Journal: Academic radiology
Published Date: Jun 29, 2024

Abstract

RATIONALE AND OBJECTIVES: Pancreas segmentation accuracy at CT is critical for the identification of pancreatic pathologies and is essential for the development of imaging biomarkers. Our objective was to benchmark the performance of five high-performing pancreas segmentation models across multiple metrics stratified by scan and patient/pancreatic characteristics that may affect segmentation performance.

Authors

  • Abhinav Suri
    Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America. Electronic address: suria@sas.upenn.edu.
  • Pritam Mukherjee
    Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA. pritam.mukherjee@nih.gov.
  • Perry J Pickhardt
    University of Wisconsin Medical School, Department of Radiology, Madison, Wisconsin, United States.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.

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