Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT.

Journal: Academic radiology
Published Date: Sep 16, 2019

Abstract

PURPOSE: Accurate pancreas segmentation has application in surgical planning, assessment of diabetes, and detection and analysis of pancreatic tumors. Factors that affect pancreas segmentation accuracy have not been previously reported. The purpose of this study is to identify technical and clinical factors that adversely affect the accuracy of pancreas segmentation on CT.

Authors

  • Mohammad Hadi Bagheri
    Clinical Image Processing Service, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892-1182, USA.
  • Holger Roth
    Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
  • William Kovacs
    Clinical Image Processing Service, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892-1182, USA.
  • Jianhua Yao
  • Faraz Farhadi
    Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
  • Xiaobai Li
    School of Cyber Science and Technology, Zhejing University, Hangzhou, China.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.

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