Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning.

Journal: Abdominal radiology (New York)
Published Date: Jun 4, 2020

Abstract

PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically.

Authors

  • Khaled Bousabarah
    University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Stereotactic and Functional Neurosurgery, Cologne, Germany. khaled.bousabarah@uk-koeln.de.
  • Brian Letzen
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, The Anlyan Center, N312A, New Haven, CT 06520.
  • Jonathan Tefera
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
  • Lynn Savic
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
  • Isabel Schobert
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
  • Todd Schlachter
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
  • Lawrence H Staib
    Biomedical Engineering, Yale University, New Haven, CT 06511, USA.
  • Martin Kocher
  • Julius Chapiro
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520. Electronic address: julius.chapiro@yale.edu.
  • MingDe Lin
    Philips Research North America, Cambridge, Massachusetts.

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