An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography

Robin Huang*, Anthony Nedanoski, David F. Fletcher, Narinder Singh, Jerome Schmid, Paul M. Young, Nicholas Stow, Lei Bi, Daniela Traini, Eugene Wong, Craig L. Phillips, Ronald R. Grunstein, Jinman Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The use of computational fluid dynamics (CFD) to model and predict surgical outcomes in the nasal cavity is becoming increasingly popular. Despite a number of well-known nasal segmentation methods being available, there is currently a lack of an automated, CFD targeted segmentation framework to reliably compute accurate patient-specific nasal models. This paper demonstrates the potential of a robust nasal cavity segmentation framework to automatically segment and produce nasal models for CFD. The framework was evaluated on a clinical dataset of 30 head Computer Tomography (CT) scans, and the outputs of the segmented nasal models were further compared with ground truth models in CFD simulations on pressure drop and particle deposition efficiency. The developed framework achieved a segmentation accuracy of 90.9 DSC, and an average distance error of 0.3 mm. Preliminary CFD simulations revealed similar outcomes between using ground truth and segmented models. Additional analysis still needs to be conducted to verify the accuracy of using segmented models for CFD purposes.

Original languageEnglish
Article number103505
Pages (from-to)1-8
Number of pages8
JournalComputers in Biology and Medicine
Volume115
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes

Keywords

  • Computational fluid dynamics
  • Computed tomography
  • Image segmentation
  • Nasal cavity

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