TIDAQUNET: tissue identification and quantification network for mid-thigh CT segmentation

Abstract

Quantification and segmentation of the mid-thigh region has high clinical importance for assessment of muscle composition and adipose tissue depositions. Changes in body composition may characterize chronic diseases like obesity, metabolic disorders, type-2 diabetes, and osteoarthritis. Effective methods for segmentation of soft and hard tissues in the mid-thigh in help to understand and characterize changes caused by disease or normal aging. The purpose of our research is to develop a fully automated system for segmentation of hard and soft tissues from CT scans of the mid-thigh region. In particular, we aim to segment the muscle, intermuscular adipose tissue, and subcutaneous adipose tissue using a deep network. A major challenge in deep learning is to provide a rich and diverse set of data for training. Another limitation in tissue segmentation applications is class imbalance, because larger structures may dominate the training process and introduce classification bias. We propose an adaptive re-sampling method according to the tissue type to address class imbalance. We evaluated the segmentation accuracy of the network by cross-validation techniques using CT scans obtained from the BLSA study. We obtained an overall DSC score of 91.5% for segmentation of the mid-thigh regional tissues. Performance evaluation results leads to the observation that our method produces very good accuracy rates and is competitive with current methods used for quantification. This method applied deep learning to a meaningful clinical application that is not revisited frequently.