An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography. (December 2019)

• Record Type: Journal Article
• Authors: Huang, Robin; Nedanoski, Anthony; Fletcher, David F.; Singh, Narinder; Schmid, Jerome; Young, Paul M.; Stow, Nicholas; Bi, Lei; Traini, Daniela; Wong, Eugene; Phillips, Craig L.; Grunstein, Ronald R.; Kim, Jinman
• Is Part Of: Computers in biology and medicine. Volume 115(2019)
• Publication Date: 2019-12
• Subjects: Nasal cavity -- Image segmentation -- Computational fluid dynamics -- Computed tomography; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103505
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: Evaluated the use of an automated nasal cavity segmentation framework for CFD. The developed framework achieved a segmentation accuracy of 90.9 DSC. Similar CFD outcomes were observed using segmented models vs ground truth models.