Pierre-Yves Baudin1, 2, Noura Azzabou3, 4, Pierre G. Carlier3, 4, Nikos Paragios1, 5
1Center for Visual Computing, Ecole Centrale Paris, Chtenay-Malabry, IDF, France; 2SIEMENS Healthcare, Saint-Denis, IDF, France; 3Institute of Myology, Paris, IDF, France; 4I2BM, MIRCen, IdM NMR Laboratory, CEA, Orsay, IDF, France; 5Equipe Galen, INRIA Saclay, Palaiseau, IDF, France
Developing an automatized tool for segmenting the different skeletal muscles in MRI with minimum user intervention is of paramount importance to facilitate muscle studies. Segmentation of skeletal muscles in 3D MRI poses some specific issues: non-discriminative appearance of the muscles, partial contours between them, large inter-subject variations, spurious contours due to fat infiltrations. We propose an automatic segmentation method based on the Random Walks algorithm to which we add a prior shape model based on learning from an annotated data set.