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Abstract #2656

Convolutional neural network automatic global segmentation of thigh muscle water-fat images in neuromuscular diseases

Harmen Reyngoudt1,2, Eduard Snezhko3, Pierre-Yves Baudin4, and Pierre G. Carlier1,2
1NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France, 2NMR Laboratory, CEA/DRF/IBFJ/MIRCen, Paris, France, 3United Institute for Informatics Problems, National Academy of Sciences, Minsk, Belarus, 4Consultants for Research in Imaging and Spectroscopy, Tournai, Belgium

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.

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