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

Huang, Robin (University of Sydney, Australia) ; Nedanoski, Anthony (University of Sydney, Australia) ; Fletcher, David F. (University of Sydney, Australia) ; Singh, Narinder (University of Sydney, Australia) ; Schmid, Jerome (Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland) ; Young, Paul M. (University of Sydney, Australia) ; Stow, Nicholas (University of Sydney, Australia) ; Bi, Lei (University of Sydney, Australia) ; Traini, Daniela (University of Sydney, Australia) ; Wong, Eugene (University of Sydney, Australia) ; Philips, Craig L. (University of Sydney, Australia) ; Grunstein, Ronald R. (University of Sydney, Australia) ; Kim, Jinman (University of Sydney, Australia)

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:
Article Type:
scientifique
Faculty:
Santé
School:
HEdS - Genève
Institute:
Aucun institut
Date:
2019-12
Pagination:
8 p.
Published in:
Computers in Biology and Medicine
Numeration (vol. no.):
2019, vol. 115, 103505
DOI:
ISSN:
0010-4825
Appears in Collection:

Note: The file is under embargo until: 2021-01-01


 Record created 2020-01-15, last modified 2020-02-05

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