Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.

Journal: European radiology
PMID:

Abstract

BACKGROUND: Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par.

Authors

  • Douwe J Spaanderman
    Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands. d.spaanderman@erasmusmc.nl.
  • Martijn P A Starmans
    Biomedical Imaging Group Rotterdam, Departments of Radiology and Nuclear Medicine Medical Informatics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands.
  • Gonnie C M van Erp
    Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • David F Hanff
    Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Judith H Sluijter
    Department of Urology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066CX, Amsterdam, The Netherlands.
  • Anne-Rose W Schut
    Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
  • Geert J L H van Leenders
    Department of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
  • Cornelis Verhoef
    Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Centre, University Medical Centre Rotterdam, the Netherlands.
  • Dirk J Grünhagen
    Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Centre, University Medical Centre Rotterdam, the Netherlands.
  • Wiro J Niessen
    Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Jacob J Visser
    Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Stefan Klein