A novel automatic segmentation method to quantify the effects of spinal cord injury on human thigh muscles and adipose tissue

Abstract

In this study, a novel automatic method for segmenting muscle groups and adipose tissue in thigh MRI volumes is developed to quantify the negative effects of spinal cord injury (SCI) on fat and muscle distribution in individuals with severe SCI. The thigh volumes were segmented based on subcutaneous fat, inter-muscular fat and muscle tissue using Linear Combination of Discrete Gaussians algorithm. Furthermore, the three main compartments of the muscle tissue: knee extensor, knee flexor and hip adductor muscles were segmented utilizing the Joint Markov Gibbs Random Field (MGRF) model that integrates first order appearance model of the muscles, spatial information, and shape model to localize the muscle groups. The method was tested on 10 SCI and 10 non-disabled (ND) subjects and the results has shown high accuracy of 96.86 ± 3.48 for fat segmentation and 94.76 ± 1.70 for muscle group segmentation based on Dice similarity percentage. Next, we calculated 3 ratios based on the volumes of the subcutaneous fat to muscle tissue, inter-muscular fat to muscle and extensor to flexor for all subjects. Mann-Whitney statistical test showed that inter-muscular fat to muscle ratio was significantly greater in SCI than in ND group (p = 0.001).