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

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

SUBMITTER: Park HJ 

PROVIDER: S-EPMC6960305 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature