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. In particular, dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% are achieved for muscle, fat, IMAT, bone, and bone marrow segmentation, 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.

通过磁共振成像（MRI）扫描对大腿组织（肌肉，脂肪，肌肉间脂肪组织（IMAT），骨骼和骨髓）进行分割，对于各种情况（例如衰老，糖尿病，肥胖症）的临床和研究研究都非常有用，代谢综合征及其相关合并症。为了实现对大腿组织的全自动，鲁棒和精确定量，我们在此基于深度网络架构设计了一种新颖的半监督分割算法。建立在提拉米苏之上分割引擎，我们提出的深度网络使用变体和经过专门设计的定向辍学来实现更快，更强的收敛，并利用多对比度MRI扫描作为输入数据。在我们的实验中，我们使用了来自巴尔的摩纵向衰老研究（BLSA）的50个不同受试者的150次扫描。拟议的系统充分利用了标记和未标记的数据进行训练，其性能优于当前的最新方法。特别是骰子得分为97.52 ％，94.61 ％，80.14 ％，95.93 ％和96.83 ％分别针对肌肉，脂肪，IMAT，骨骼和骨髓进行分割。我们的结果表明，所提出的系统可用于临床研究，在这些研究中，体积和分布组织的量化至关重要，而标记是一个重要的问题。据我们所知，该系统是首次尝试使用多端大腿MRI扫描的单端到端半监督深度学习框架进行多组织分割。