3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 8, 2021

Abstract

BACKGROUND AND OBJECTIVES: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer.

Authors

  • Maureen van Eijnatten
    Amsterdam UMC and Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam, Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovation Lab, Amsterdam Movement Sciences, de Boelelaan 1117, Amsterdam, the Netherlands; Centrum Wiskunde & Informatica (CWI), Science Park 123, Amsterdam, the Netherlands.
  • 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.
  • K Joost Batenburg
    Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Mathematical Institute, Leiden University, 2300 RA Leiden, the Netherlands.
  • Felix Lucka
  • Emma Beddowes
    Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, CB2 0QQ Cambridge, United Kingdom.
  • Carlos Caldas
    Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, CB2 0QQ Cambridge, United Kingdom.
  • Ferdia A Gallagher
  • 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.
  • 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.

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