Deep learning-based segmentation of multisite disease in ovarian cancer.

Journal: European radiology experimental
Published Date:

Abstract

PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

Authors

  • Thomas Buddenkotte
    Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany. Electronic address: t.buddenkotte@uke.de.
  • Leonardo Rundo
    Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK. Electronic address: lr495@cam.ac.uk.
  • Ramona Woitek
    Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria. Electronic address: rw585@cam.ac.uk.
  • Lorena Escudero Sanchez
    Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom.
  • Lucian Beer
    Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria. Electronic address: lb795@cam.ac.uk.
  • Mireia Crispin-Ortuzar
    Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK. Electronic address: mireia.crispinortuzar@cruk.cam.ac.uk.
  • Christian Etmann
    Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.
  • Subhadip Mukherjee
    Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
  • Vlad Bura
    Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
  • Cathal McCague
    Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom.
  • Hilal Sahin
    Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
  • Roxana Pintican
    Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania.
  • Marta Zerunian
    Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
  • Iris Allajbeu
    Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
  • Naveena Singh
    Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
  • Anju Sahdev
    Department of Radiology, Barts Health NHS Trust, London, UK.
  • Laura Havrilesky
    Duke University Medical Center, Durham, NC, USA.
  • David E Cohn
    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, The Ohio State University College of Medicine, Columbus, Ohio. Electronic address: David.Cohn@osumc.edu.
  • Nicholas W Bateman
    Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
  • Thomas P Conrads
    Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
  • Kathleen M Darcy
    Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
  • G Larry Maxwell
    Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
  • John B Freymann
    Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
  • Ozan Oktem
  • James D Brenton
    Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK. Electronic address: james.brenton@cruk.cam.ac.uk.
  • Evis Sala
    Department of Radiology and Cancer Research UK Cambridge Centre, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, England.
  • Carola-Bibiane Schönlieb
    EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK.