Keywords: Machine Learning/Artificial Intelligence, Muscle, SegmentationQuantitative magnetic resonance imaging offers promising surrogate biomarkers for the early diagnosis and monitoring of pathological changes in leg muscles in patients with neuromuscular disorders. To use this in a clinical routine, automatic segmentation of muscles is needed. To investigate precision and accuracy of various Computational neuronal networks for automated segmentation of upper leg muscles, different loss function were used and quantitative outcome compared to the gold-standard of manual segmentation. The best segmentation results were achieved, with the Fokal Tversky loss function and a single input approach.
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