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

Robin Huang1, Anthony Nedanoski2, David F Fletcher3

  • 1School of Computer Science, University of Sydney, Australia.

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Summary

A new automated framework accurately segments nasal cavity models from CT scans for computational fluid dynamics (CFD) analysis. This improves patient-specific modeling for predicting surgical outcomes.

Keywords:
Computational fluid dynamicsComputed tomographyImage segmentationNasal cavity

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Fluid Dynamics

Background:

  • Computational fluid dynamics (CFD) is increasingly used to model nasal cavity surgical outcomes.
  • Existing nasal segmentation methods lack automation for CFD-targeted, patient-specific models.

Purpose of the Study:

  • To demonstrate a robust, automated framework for nasal cavity segmentation.
  • To generate patient-specific nasal models suitable for CFD analysis.

Main Methods:

  • Developed an automated nasal cavity segmentation framework.
  • Evaluated the framework on 30 clinical head CT scans.
  • Compared segmented models with ground truth models using CFD simulations for pressure drop and particle deposition.

Main Results:

  • Achieved 90.9% Dice Similarity Coefficient (DSC) for segmentation accuracy.
  • Obtained an average distance error of 0.3 mm.
  • Preliminary CFD simulations showed comparable outcomes between segmented and ground truth models.

Conclusions:

  • The developed framework shows potential for accurate, automated nasal cavity segmentation for CFD.
  • Further analysis is required to fully validate the use of segmented models in CFD simulations.