Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Syed Muhammad Anwar,
Ismail Irmakci,
Drew A. Torigian
et al.
Abstract:Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon … Show more
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