Work

Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results

Year: 2019

Type: preprint

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 a new tissue signature model of muscular dystrophy with the MPDL algorithm segments different tissue types with excellent results.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1908.00175

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

Institutions: Johns Hopkins University, Kennedy Krieger Institute, Johns Hopkins Medicine, Sidney Kimmel Comprehensive Cancer Center

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Nvidia, National Institutes of Health