Accurate thigh inter-muscular adipose quantification using a data-driven and sparsity-constrained deformable model

The thigh inter-muscular adipose tissue (IMAT) quantification plays a critical role in various medical analysis tasks, e.g., the analysis of physical performance or the diagnose of knee osteoarthritis. In recent years, several techniques have been proposed to perform automated thigh tissues quantification. However, nobody has provided effective methods to track fascia lata, which is an important anatomic trail to distinguish between subcutaneous adipose tissue (SAT) and I-MAT in thigh. As a result, the estimation of IMAT may not be accurate for subjects with pathological conditions. On the other hand, tissue prior information, e.g., intensity, orientation and scale, becomes critical to infer and refine the fascia lata boundary from image appearance cues. In this paper, we propose a novel data-driven and sparsity-constrained de-formable model to obtain accurate fascia lata labeling. The model deformation is driven by the target points on fascia lata detected by a local discriminative classifier in a narrowband fashion. By using a sparsity-constrained optimization, the deformation is solved with errors and outliers suppression. The proposed approach has been evaluated on a set of 3D MR thigh volumes. In a comparison with another state-of-art framework, our approach produces superior performance.