Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；The Johns Hopkins University（约翰斯·霍普金斯大学）；Kennedy Krieger Center（肯尼迪·克里格中心）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
