Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI
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...
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| Published in: | Journal of magnetic resonance imaging Vol. 50; no. 4; pp. 1169 - 1181 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Hoboken, USA
John Wiley & Sons, Inc
01.10.2019
Wiley Subscription Services, Inc |
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| ISSN: | 1053-1807, 1522-2586, 1522-2586 |
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| Abstract | 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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| AbstractList | 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.
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.
Retrospective study aimed to evaluate a technical development.
Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis.
1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence.
Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O
) and hyperoxic (100% O
) 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.
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.
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.
DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI.
2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181. 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. BackgroundUltrashort 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.PurposeTo 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 TypeRetrospective study aimed to evaluate a technical development.PopulationForty‐five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis.Field Strength/Sequence1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence.AssessmentTwo 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 TestsRelative 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.ResultsThe 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 ConclusionDL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI.Level of Evidence: 2Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2019;50:1169–1181. 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.BACKGROUNDUltrashort 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.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.PURPOSETo 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.Retrospective study aimed to evaluate a technical development.STUDY TYPERetrospective study aimed to evaluate a technical development.Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis.POPULATIONForty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis.1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence.FIELD STRENGTH/SEQUENCE1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence.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.ASSESSMENTTwo 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.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.STATISTICAL TESTSRelative 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.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.RESULTSThe 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.DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI.DATA CONCLUSIONDL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI.2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181.LEVEL OF EVIDENCE2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181. |
| Author | Fain, Sean B. Schiebler, Mark L. Nagle, Scott K. Liu, Fang Zha, Wei Evans, Michael D. |
| Author_xml | – sequence: 1 givenname: Wei orcidid: 0000-0002-6914-8092 surname: Zha fullname: Zha, Wei email: lynnzha@gmail.com organization: University of Wisconsin‐Madison – sequence: 2 givenname: Sean B. surname: Fain fullname: Fain, Sean B. organization: University of Wisconsin‐Madison – sequence: 3 givenname: Mark L. surname: Schiebler fullname: Schiebler, Mark L. organization: University of Wisconsin‐Madison – sequence: 4 givenname: Michael D. surname: Evans fullname: Evans, Michael D. organization: University of Minnesota Twin Cities – sequence: 5 givenname: Scott K. surname: Nagle fullname: Nagle, Scott K. organization: University of Wisconsin‐Madison – sequence: 6 givenname: Fang orcidid: 0000-0001-8032-6681 surname: Liu fullname: Liu, Fang organization: University of Wisconsin‐Madison |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30945385$$D View this record in MEDLINE/PubMed |
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| Keywords | lung deep learning magnetic resonance imaging asthma hyperoxia cystic fibrosis |
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Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development... Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid... BackgroundUltrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development... |
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| SubjectTerms | Adult Artificial neural networks Asthma Asthma - diagnostic imaging Asthma - physiopathology Automation Biomarkers Coders Confidence intervals Cystic fibrosis Cystic Fibrosis - diagnostic imaging Cystic Fibrosis - physiopathology deep learning Female Field strength Humans hyperoxia Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Imaging, Three-Dimensional - methods lung Lung - diagnostic imaging Lung - physiopathology Lungs Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Neural networks Neural Networks, Computer Protons Rank tests Regional development Respiratory function Retrospective Studies Statistical analysis Statistical tests Structure-function relationships |
| Title | Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI |
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