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 Tiramisu segmentation
engine, our proposed deep networks use variational and specially designed
targeted dropouts for faster and robust convergence, and utilize multi-contrast
MRI scans as input data. In our experiments, we have used 150 scans from 50
distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The
proposed system made use of both labeled and unlabeled data with high efficacy
for training, and outperformed the current state-of-the-art methods with dice
scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT,
bone, and bone marrow tissues, respectively. Our results indicate that the
proposed system can be useful for clinical research studies where volumetric
and distributional tissue quantification is pivotal and labeling is a
significant issue. To the best of our knowledge, the proposed system is the
first attempt at multi-tissue segmentation using a single end-to-end
semi-supervised deep learning framework for multi-contrast thigh MRI scans.Comment: 20 pages, 9 figures, Journal of Signal Processing System
