Keywords: Analysis/Processing, Segmentation
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.
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