Recent evidence suggests that abdominal CT-based measurements of muscle mass correlate well with the actual muscle mass ( 10, 11), and abdominal CT has also been proven as an accurate method for quantitative assessment of abdominal visceral fat ( 12).

Recently, sarcopenia (the loss of skeletal muscle mass) has emerged as a biomarker for assessing the prognosis in various cancer patients ( 3, 4, 5), morbidity/mortality rate after major surgery ( 6, 7, 8), and general health status ( 9).ĬT is a useful tool for evaluating body muscle and fat, as areas of muscle and fat tissue can be accurately visualized on the basis of CT attenuation values. Increasing rates of occurrence of obesity have been observed in the past few decades, and extensive research has been conducted to explore the relationship between body fat and various diseases ( 1, 2). Quantification of the muscle and fat mass of the body, which is referred to as body morphometry, is an essential part of health evaluation. Hyo Jung Park, MD, 1 Yongbin Shin, PhD, 2 Jisuk Park, MS, 1 Hyosang Kim, MD, PhD, 3 In Seob Lee, MD, PhD, 4 Dong-Woo Seo, MD, PhD, 5 Jimi Huh, MD, PhD, 6 Tae Young Lee, MD, 7 TaeYong Park, PhD, 2 Jeongjin Lee, PhD, 2ġDepartment of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ĢSchool of Computer Science and Engineering, Soongsil University, Seoul, Korea.ģDepartment of Nephrology, Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ĤDepartment of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ĥDepartment of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ĦDepartment of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Ajou University Hospital, Suwon, Korea.ħDepartment of Radiology, Ulsan University Hospital, Ulsan, Korea. English.Ĭopyright © 2020 The Korean Society of Radiologyĭevelopment and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography

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