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Segmentation of the upper airway using deep learning - nnUNet.

Silvia Gianoni-Capenakas¹, Alejandro Matos², Gauthier Dot³

¹Registered Orthodontist. Clinical Assistant Professor, Mike Petric School of Dentistry, University of Alberta. Kaye Edmonton Clinic. 11400 University Ave, 8th floor. Edmonton, AB, T6G 1Z1, Canada.

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Summary

A deep learning model automates upper airway segmentation from CBCT and CT scans, achieving high accuracy and reducing analysis time. This efficient tool aids clinical decisions and research across diverse patient groups.

Keywords:

Airway obstruction Computer tomography Deep Learning Image interpretation Machine learning Neural Networks

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Manual segmentation of the upper airway is time-consuming and prone to inter-observer variability.
Accurate 3D analysis of the upper airway is crucial for diagnosis and treatment planning.

Purpose of the Study:

To develop and evaluate a deep learning framework for automated 3D upper airway segmentation.
To assess the model's performance on multi-source CBCT and CT datasets.

Main Methods:

A dataset of 220 multi-source 3D images (CBCT, CT) from diverse populations was used.
A multi-source training approach with nnUNet was employed.
One-center-out validation was performed on CBCT scans.

Main Results:

The multi-source deep learning model achieved a high average Dice score of 0.962.
The nnUNet-155 model showed a 3.31% absolute volume difference compared to manual segmentation.
Prediction time was reduced to 5 minutes per volume.

Conclusions:

The developed deep learning model offers a robust, efficient, and generalizable solution for automated upper airway segmentation.
This tool provides precise, consistent, and time-saving 3D analysis, supporting clinical decision-making and research.
The model overcomes manual segmentation limitations, enhancing reliability across diverse patient demographics and imaging modalities.