Rationale and Objectives
Accurate segmentation of the upper airway lumen and surrounding soft tissue anatomy,
especially tongue fat, using magnetic resonance images is crucial for evaluating the
role of anatomic risk factors in the pathogenesis of obstructive sleep apnea (OSA).
We present a convolutional neural network to automatically segment and quantify upper
airway structures that are known OSA risk factors from unprocessed magnetic resonance
images.
Materials and Methods
Four datasets (n = [31, 35, 64, 76]) with T1-weighted scans and manually delineated labels of 10 regions
of interest were used for model training and validations. We investigated a modified
U-Net architecture that uses multiple convolution filter sizes to achieve multi-scale
feature extraction. Validations included four-fold cross-validation and leave-study-out
validations to measure generalization ability of the trained models. Automatic segmentations
were also used to calculate the tongue fat ratio, a biomarker of OSA. Dice coefficient,
Pearson's correlation, agreement analyses, and expert-derived clinical parameters
were used to evaluate segmentations and tongue fat ratio values.
Results
Cross-validated mean Dice coefficient across all regions of interests and scans was
0.70 ± 0.10 with highest mean Dice coefficient in the tongue (0.89) and mandible (0.81).
The accuracy was consistent across all four folds. Also, leave-study-out validations
obtained comparable accuracy across uniquely acquired datasets. Segmented volumes
and the derived tongue fat ratio values showed high correlation with manual measurements,
with differences that were not statistically significant (p < 0.05).
Conclusion
High accuracy of automated segmentations indicate translational potential of the proposed
method to replace time consuming manual segmentation tasks in clinical settings and
large-scale research studies.
Key Words
Abbreviations:
OSA (obstructive sleep apnea), MRI (magnetic resonance imaging), MR (magnetic resonance), DL (deep learning), CNN (convolutional neural network), ROI (region of interest), V1 (voxel-weighted at max fat pad intensity), V2 (voxel-weighted at 99th percentile fat pad intensity)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: May 20, 2022
Accepted:
April 25,
2022
Received in revised form:
April 19,
2022
Received:
March 20,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
