Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 1, 2020

Abstract

BACKGROUND AND OBJECTIVE: Performing patient-specific, pre-operative cochlea CT-based measurements could be helpful to positively affect the outcome of cochlear surgery in terms of intracochlear trauma and loss of residual hearing. Therefore, we propose a method to automatically segment and measure the human cochlea in clinical ultra-high-resolution (UHR) CT images, and investigate differences in cochlea size for personalized implant planning.

Authors

  • Floris Heutink
    Department of Otorhinolaryngology and Donders Institute for Brain, Cognition and Behavior, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
  • Valentin Koch
    Department of Radiology and Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
  • Berit Verbist
    Department of Radiology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands.
  • Willem Jan van der Woude
    Department of Radiology and Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
  • Emmanuel Mylanus
    Department of Otorhinolaryngology and Donders Institute for Brain, Cognition and Behavior, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
  • Wendy Huinck
    Department of Otorhinolaryngology and Donders Institute for Brain, Cognition and Behavior, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
  • Ioannis Sechopoulos
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Marco Caballo
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.

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