• Andrew T Grainger, Arun Krishnaraj, Michael H Quinones, Nicholas J Tustison, Samantha Epstein, Daniela Fuller, Aakash Jha, Kevin L Allman, and Weibin Shi.
• Departments of Biochemistry & Molecular Genetics, Richmond, Virginia.
• Acad Radiol. 2020 Aug 5.

Rationale And ObjectivesDevelop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images.Materials And MethodsSequential CT images spanning the abdominal region of seven subjects were manually segmented to calculate subcutaneous fat (SAT) and visceral fat (VAT). The resulting segmentation maps of SAT and VAT were augmented using a template-based data augmentation approach to create a large dataset for neural network training. Neural network performance was evaluated on both sequential CT slices from three subjects and randomly selected CT images from the upper, central, and lower abdominal regions of 100 subjects.ResultsBoth subcutaneous and abdominal cavity segmentation images created by the two methods were highly comparable with an overall Dice similarity coefficient of 0.94. Pearson's correlation coefficients between the subcutaneous and visceral fat volumes quantified using the two methods were 0.99 and 0.99 and the overall percent residual squared error were 5.5% and 8.5%. Manual segmentation of SAT and VAT on the 555 CT slices used for testing took approximately 46 hours while automated segmentation took approximately 1 minute.ConclusionOur data demonstrates that deep learning methods utilizing a template-based data augmentation strategy can be employed to accurately and rapidly quantify total abdominal SAT and VAT with a small number of training images.Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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