A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans
Body composition on chest CT scans encompasses a set of important imaging biomarkers. This study developed and validated a fully automated analysis pipeline for multi-vertebral level assessment of muscle and adipose tissue on routine chest CT scans. This study retrospectively trained two convolution...
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Published in | Radiology. Artificial intelligence Vol. 4; no. 1; p. e210080 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Radiological Society of North America
01.01.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Body composition on chest CT scans encompasses a set of important imaging biomarkers. This study developed and validated a fully automated analysis pipeline for multi-vertebral level assessment of muscle and adipose tissue on routine chest CT scans. This study retrospectively trained two convolutional neural networks on 629 chest CT scans from 629 patients (55% women; mean age, 67 years ± 10 [standard deviation]) obtained between 2014 and 2017 prior to lobectomy for primary lung cancer at three institutions. A slice-selection network was developed to identify an axial image at the level of the fifth, eighth, and 10th thoracic vertebral bodies. A segmentation network (U-Net) was trained to segment muscle and adipose tissue on an axial image. Radiologist-guided manual-level selection and segmentation generated ground truth. The authors then assessed the predictive performance of their approach for cross-sectional area (CSA) (in centimeters squared) and attenuation (in Hounsfield units) on an independent test set. For the pipeline, median absolute error and intraclass correlation coefficients for both tissues were 3.6% (interquartile range, 1.3%-7.0%) and 0.959-0.998 for the CSA and 1.0 HU (interquartile range, 0.0-2.0 HU) and 0.95-0.99 for median attenuation. This study demonstrates accurate and reliable fully automated multi-vertebral level quantification and characterization of muscle and adipose tissue on routine chest CT scans.
: Skeletal Muscle, Adipose Tissue, CT, Chest, Body Composition Analysis, Convolutional Neural Network (CNN), Supervised Learning
© RSNA, 2022. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author contributions: Guarantors of integrity of entire study, C.P.B., T.D.B., F.J.F.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, C.P.B., T.D.B., F.J.F.; clinical studies, T.D.B., K.M., C.J., J.H.C., F.J.F.; experimental studies, C.P.B., T.D.B., M.M.W., K.M., K.P.A., F.J.F.; statistical analysis, C.P.B., T.D.B., J.P.M., K.P.A., F.J.F.; and manuscript editing, C.P.B., T.D.B., M.M.W., J.P.M., K.M., J.H.C., J.K., K.P.A., F.J.F. |
ISSN: | 2638-6100 2638-6100 |
DOI: | 10.1148/ryai.210080 |