CNN for Automatic Estimation of Paraspinal Skeletal Muscle Index for Assessment of Sarcopenia and Correlation with Liver Frailty

Motivation: Paraspinal muscle mass estimation for liver transplant candidacy is practically limited by tedious segmentation. Goal(s): Develop an automatic segmentation algorithm using a convolutional neural network (CNN) for segmentation of abdominal paraspinal muscles to calculate skeletal muscle index in cirrhotic patients. Approach: A U-Net CNN was trained on spin echo images and evaluated with Dice coefficient. Skeletal muscle index of original and predicted masks was compared with independent t-test, ANOVA and a Bland-Altman plot. Results: Dice coefficient was >0.88, with a mean bias of <1% between CNN SMI and manual SMI, while not being statistically significant. SMI and liver frailty were not directly associated. Impact: Faster and precise segmentation of abdominal paraspinal muscles to calculate muscle mass in cirrhotic patients would reduce the time burden, thereby increasing practicality for MRI skeletal muscle index estimation.