A detection-driven and sparsity-constrained deformable model for fascia lata labeling and thigh inter-muscular adipose quantification

Chaowei Tan, Kang Li, Zhennan Yan, Dong Yang, Shaoting Zhang, Hui Jing Yu, Klaus Engelke, Colin Miller, Dimitris Metaxas

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Quantification of the thigh inter-muscular adipose tissue (IMAT) plays a critical role in various medical data analysis tasks, e.g., the analysis of physical performance or the diagnosis of knee osteoarthritis. Several techniques have been proposed to perform automated thigh tissues quantification. However, none of them has provided an effective method to track fascia lata, which is an important anatomic trail to distinguish between subcutaneous adipose tissue (SAT) and IMAT in the thigh. As a result, the estimates of IMAT may not be accurate due to the unclear appearance cues, complicated anatomic, or pathological characteristics of the fascia lata. Thus, prior tissue information, e.g., intensity, orientation and scale, becomes critical to infer the fascia lata location from magnetic resonance (MR) images. In this paper, we propose a novel detection-driven and sparsity-constrained deformable model to obtain accurate fascia lata labeling. The model's deformation is driven by the detected control points on fascia lata through a discriminative detector in a narrow-band fashion. By using a sparsity-constrained optimization, the deformation is solved from errors and outliers suppression. The proposed approach has been evaluated on a set of 3D MR thigh volumes. In a comparison with the state-of-the-art framework, our approach produces superior performance.

Original languageEnglish (US)
Pages (from-to)80-89
Number of pages10
JournalComputer Vision and Image Understanding
Volume151
DOIs
StatePublished - Oct 1 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

Keywords

  • Deformable model
  • Fascia lata
  • Learning-based and narrow-band Detection
  • Sparsity-constrained optimization
  • Thigh inter-muscular adipose tissue quantification

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