Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results

Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner, Shivani Ahlawat. Vladimir Braverman, Doris G. Leung, Michael A. Jacobs,

Keywords: Deep learning, Machine learning, CNN, Magnetic resonance imaging, Multiparametric MRI, Muscular dystrophy, Tissue signature vector

TL;DR: Deep Learning Segmentation of Muscular Dystrophy

Abstract: A current clinical challenge is identifying limb girdle muscular dystrophy 2I (LGMD2I) tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle tissue. Deep learning algorithms have the ability to learn different features by using the inherent tissue contrasts from multiparametric magnetic resonance imaging (mpMRI). To that end, we developed a novel multiparametric deep learning network (MPDL) tissue signature model based on mpMRI and applied it to LGMD2I. We demonstrate that the new tissue signature model of muscular dystrophy with the MPDL algorithm segments different tissue types with excellent results.

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