Quantification of Fat Fraction (FF) in individual lower limb muscles of patients with neuromuscular disorders relies on segmentation. Few studies have indicated that Fully Convolutional Networks (FCNs) can provide reliable automatic segmentations to replace manual tasks. However, their sensitivity to fat infiltration has never been accurately assessed. Four FCN were benchmarked for the segmentation of 114 thigh and 108 calf images (1788 muscles) with FF up to 60%. HRNet was the only network that didn't show any segmentation failures. DSC obtained was comparable to other networks. The FF values calculated from the automatic (FFa) and manual (FFm) segmentations were consistent.
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