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Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE
Wei Zha1, Sean B Fain1,2,3, Mark L Schiebler2
1Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Background:
Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor-intensive lung segmentation.
Purpose:
To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI.
Study Type:
Retrospective study aimed to evaluate a technical development.
Population:
Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis.
Field Strength/Sequence:
1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence.
Assessment:
Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O2 ) and hyperoxic (100% O2 ) conditions. Automated segmentation of the lungs using 2D convolutional encoder-decoder based DL method, and the subsequent functional quantification via adaptive K-means were compared with the results obtained from the reference method, supervised region growing.
Statistical Tests:
Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two-sided Wilcoxon signed-rank test for computation time, and Bland-Altman analysis on the functional measure derived from the OE images.
Results:
The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland-Altman analysis showed nonsignificant intermethod differences of volumetric (P ≥ 0.12) and functional measurements (P ≥ 0.34) in the left and right lungs.
Data Conclusion:
DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI.
Level Of Evidence:
2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181.
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