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

doi:10.1016/j.compbiomed.2019.103505

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

Comput Biol Med. 2019 Dec:115:103505. doi: 10.1016/j.compbiomed.2019.103505. Epub 2019 Oct 16.

Authors

Robin Huang¹, Anthony Nedanoski², David F Fletcher³, Narinder Singh⁴, Jerome Schmid⁵, Paul M Young⁶, Nicholas Stow⁶, Lei Bi⁷, Daniela Traini⁶, Eugene Wong⁴, Craig L Phillips⁸, Ronald R Grunstein⁸, Jinman Kim⁷

Affiliations

¹ School of Computer Science, University of Sydney, Australia. Electronic address: [email protected].
² School of Mechanical and Aerospace Engineering, University of Sydney, Australia; Discipline of Pharmacology, Faculty of Medicine and Heath and Woolcock Institute of Medical Research, University of Sydney, Australia.
³ School of Chemical and Molecular Engineering, University of Sydney, Australia.
⁴ Department of Otolaryngology, Westmead Hospital, University of Sydney, Australia.
⁵ Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Switzerland.
⁶ Discipline of Pharmacology, Faculty of Medicine and Heath and Woolcock Institute of Medical Research, University of Sydney, Australia.
⁷ School of Computer Science, University of Sydney, Australia.
⁸ CIRUS, Sleep and Circadian Group, Woolcock Institute of Medical Research and Faculty of Medicine and Health, University of Sydney, Australia.

PMID: 31704374
DOI: 10.1016/j.compbiomed.2019.103505

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.

Keywords: Computational fluid dynamics; Computed tomography; Image segmentation; Nasal cavity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computer Simulation*
Female
Humans
Hydrodynamics*
Male
Models, Biological*
Nasal Cavity* / diagnostic imaging
Nasal Cavity* / physiology
Nose
Tomography, X-Ray Computed*