Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography

  • Hyo Jung Park (Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Yongbin Shin (School of Computer Science and Engineering, Soongsil University) ;
  • Jisuk Park (Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Hyosang Kim (Department of Nephrology, Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • In Seob Lee (Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Dong-Woo Seo (Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Jimi Huh (Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Ajou University Hospital) ;
  • Tae Young Lee (Department of Radiology, Ulsan University Hospital) ;
  • TaeYong Park (School of Computer Science and Engineering, Soongsil University) ;
  • Jeongjin Lee (School of Computer Science and Engineering, Soongsil University) ;
  • Kyung Won Kim (Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine)
  • Received : 2019.06.26
  • Accepted : 2019.10.15
  • Published : 2020.01.01
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Abstract

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

Keywords

Acknowledgement

This study was supported by a grant of the Korea Health Industry Development Institute (No. HI18C1216) and a grant of the National Research Foundation of Korea (No. 2017R1A2B3011475).

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