Abstract

The quantitative assessment of metabolic bone diseases relies on tissue properties such as bone mineral density (BMD) and bone microarchitecture. In spite of an increasing number of publications using high-resolution peripheral quantitative computed-tomography (HR-pQCT), the accurate and reproducible separation of cortical and trabecular bone remains challenging. In this paper, we present a novel, fully automated, threshold-independent technique for the segmentation of cortical and trabecular bone in HR-pQCT scans. This novel post-processing method is based on modeling appearance characteristics from manually annotated cases. In our experiments the algorithm automatically selected texture features with high differentiating power and trained a classifier to separate cortical and trabecular bone. From this mask, cortical thickness and tissue volume could be calculated with high accuracy. The overlap between the proposed threshold-independent segmentation tool (TIST) and manual contouring was 0.904±0.045 (Dice coefficient). In our experiments, TIST obtained higher overall accuracy in our measurements than other techniques.

Links

Publisher Full Text

Authors

Valentinitsch A, Patsch JM, Deutschmann J, Schueller-Weidekamm C, Resch H, Kainberger F, Langs G

Institution

Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, 1090 Vienna, Austria. alexander.valentinitsch@meduniwien.ac.at

Source

Bone 51:3 2012 Sep pg 480-7

MeSH

Aged
Aged, 80 and over
Algorithms
Automation
Female
Humans
Imaging, Three-Dimensional
Male
Middle Aged
Organ Size
Radius
Regression Analysis
Tissue Donors
Tomography, X-Ray Computed

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22705149