Radiol Artif Intell 2021 Mar 6;3(2):e200130. Epub 2021 Jan 6.
Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5031, Cincinnati, OH 45229-3026 (J.C., E.S., L.A.G., A.T.T., S.B.); and Departments of Radiology (E.S., A.T.T., S.B.) and Pediatrics (A.T.T.), University of Cincinnati College of Medicine, Cincinnati, Ohio.
Purpose: To automate skeletal muscle segmentation in a pediatric population using convolutional neural networks that identify and segment the L3 level at CT.
Materials And Methods: In this retrospective study, two sets of U-Net-based models were developed to identify the L3 level in the sagittal plane and segment the skeletal muscle from the corresponding axial image. For model development, 370 patients (sampled uniformly across age group from 0 to 18 years and including both sexes) were selected between January 2009 and January 2019, and ground truth L3 location and skeletal muscle segmentation were manually defined. Read More