Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction.

Journal: European radiology
PMID: 37071168

Abstract

OBJECTIVES: Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters.

Authors

  • Ivan Dudurych
    Department of Radiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands. i.dudurych@umcg.nl.
  • Antonio Garcia-Uceda
    Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.
  • Jens Petersen
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Yihui Du
    University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
  • Rozemarijn Vliegenthart
    University of Groningen, University Medical Center Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ Groningen, The Netherlands.
  • Marleen de Bruijne

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