Abstract
In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.
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Baudin, P.Y., Azzabou, N., Carlier, P.G., Paragios, N. (2012). Prior Knowledge, Random Walks and Human Skeletal Muscle Segmentation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_70
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DOI: https://doi.org/10.1007/978-3-642-33415-3_70
Publisher Name: Springer, Berlin, Heidelberg
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