Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography

Objective: In this paper we introduce a methodology for hard and soft tissue quantification at proximal, intermediate and distal tibia sites using peripheral quantitative computed tomography scans. Quantification of bone properties is crucial for estimating bone structure resistance to mechanical stress and adaptations to loading. Soft tissue variables can be computed to investigate muscle volume and density, muscle-bone relationship, and fat infiltration. Approach: We employed implicit active contour models and clustering techniques for automated segmentation and identification of bone, muscle and fat at 4%, 38%, and 66% tibia length. Next, we calculated densitometric, area and shape characteristics for each tissue type. We implemented our approach as a multi-platform tool denoted by TIDAQ (tissue identification and quantification) to be used by clinical researchers. Main results: We validated the proposed method against reference quantification measurements and tissue delineations obtained by semi-automated workflows. The average Deming regression slope between the tested and reference method was 1.126 for cross-sectional areas and 1.078 for mineral densities, indicating very good agreement. Our method produced high average coefficient of variation (R-2) estimates: 0.935 for cross-sectional areas and 0.888 for mineral densities over all tibia sites. In addition, our tissue segmentation approach achieved an average Dice coefficient of 0.91 over soft and hard tissues, indicating very good delineation accuracy. Significance: Our methodology should allow for high throughput, accurate and reproducible automatic quantification of muscle and bone characteristics of the lower leg. This information is critical to evaluate risk of future adverse outcomes and assess the effect of medications, hormones, and behavioral interventions aimed at improving bone and muscle strength.