Korean J Radiol.  2020 Jan;21(1):88-100. 10.3348/kjr.2019.0470.

Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. medimash@gmail.com
  • 2School of Computer Science and Engineering, Soongsil University, Seoul, Korea.
  • 3Department of Nephrology, Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • 4Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • 5Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • 6Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Ajou University Hospital, Suwon, Korea.
  • 7Department of Radiology, Ulsan University Hospital, Ulsan, Korea.

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.

Keyword

Deep learning; Artificial intelligence; Sarcopenia; Muscles; Adipose tissue
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