Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.

Journal: Medical physics
Published Date: Mar 16, 2021

Abstract

PURPOSE: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset.

Authors

  • Ananya Panda
    Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
  • Panagiotis Korfiatis
    From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
  • Garima Suman
    Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
  • Sushil K Garg
    Department of Gastroenterology and Hepatology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
  • Eric C Polley
  • Dhruv P Singh
    Department of Gastroenterology and Hepatology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
  • Suresh T Chari
    Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
  • Ajit H Goenka
    Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: goenka.ajit@mayo.edu.

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