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A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and
Christopher P Bridge1, Till D Best1, Maria M Wrobel1
1Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science (C.P.B., J.K.C., K.P.A.); Martinos Center for Biomedical Imaging, Department of Radiology (C.P.B, K.P.A.); Division of Thoracic Imaging and Intervention (T.D.B., M.M.W., J.P.M., F.J.F.), Department of Radiology, Massachusetts General Hospital; and Department of Radiology, Brigham and Women's Hospital, (K.P.A.), 55 Fruit St, Boston, MA 02114; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany (T.D.B.); Department of Radiology, Berlin Institute of Health, Berlin, Germany (T.D.B.); Department of Radiology, Ludwig Maximilian University, Munich, Germany (M.M.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (K.M.); Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St Louis, Mo (C.J.); and Departments of Medicine and Radiology, University of Chicago, Chicago, Ill (J.H.C.).
Abstract:
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. Keywords: Skeletal Muscle, Adipose Tissue, CT, Chest, Body Composition Analysis, Convolutional Neural Network (CNN), Supervised Learning Supplemental material is available for this article. © RSNA, 2022.
