Manual segmentation of skeletal muscles in quantitative NMRI studies is a laborious task. In this work, deep learning using a convolutional neural network (CNN) was applied for segmenting the global thigh segment and assessing the muscle fatty replacement over 1 year in patients with several neuromuscular pathologies. A series of 425 Dixon data sets, obtained at 3 T, were used for this purpose. Dice coefficients of 0.97 were obtained when comparing manual and CNN based segmentation. Standardized response means for the fat fraction evolution over 1 year using CNN were at least as high as results obtained with manual segmentation.