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Abstract #2800

Convolutional neural network segmentation of skeletal muscle NMR images

Eduard Snezhko1, Noura Azzabou2,3, Pierre-Yves Baudin4, and Pierre G. Carlier2,3

1Mathematical Cybernetics, United Institute of Informatics Problems, Minsk, Belarus, 2NMR Laboratory, Institute of Myology, Paris, France, 3NMR Laboratory, CEA,DRF,IBFJ,MIRCen, Paris, France, 4Consultants for Research in Imaging and Spectroscopy, Tournai, Belgium

The purpose of this work was to investigate the ability of deep convolutional neural networks (CNN) to segment muscle groups in NMR images. To this end, we used lower limb scans of patients with different neuromuscular diseases and various levels of fatty infiltration. Thigh and leg muscle groups were first segmented manually and then used in the training and validation processes of the CNN. The mean Dice coefficient of the obtained segmentations was 0.9, demonstrating the effectiveness of the technique in automatically segmenting both healthy and pathological muscle groups.

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