Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures

Abstract

Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures.

Introduction: Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures.

Methods: In total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation.

Results: The random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64).

Conclusion: The presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.

Keywords: BMD; Machine learning; Opportunistic screening; Osteoporosis; Quantitative computed tomography; Random forest model; Texture analysis; Vertebral fractures.

Fig. 1

Region definition process. (a) The biggest sphere, fitting in the mask defined the center point of the vertebral body. Additionally, we extracted surface points of the vertebral endplates, which we projected to the center point. (b) The given set of 3D points was used to compute the three orthogonal planes: superior-inferior plane (i.e., fitted plane), anterior-posterior plane, and medial-lateral plane. (c) The intersections resulted in 27 regions

Fig. 2

The mean volumetric density distribution (vBMD) of the thoracic and lumbar spine in comparison with the FX and noFX group

Fig. 3

Feature selection and importance. a Classification performance using feature selection. The ranked features according to the Gini importance (GI) are selected a in a 2ⁿ fashion (i.e., 2, 4, 8, … .32768). The performance (AUC) of a fourfold cross-validation has been plotted for the increasing amount of selected features. The vertex (red dot) is used as the optimal cut of the fitted quadratic function (i.e., parabola) representing the overall performance of 0.88 AUC. b Composition of the set of important features. The mean Gini importance for each feature class of density and texture features is reported. Density features are split into global (vertebral level (vBMD)) and local features (sub-region level (BMDr)). c Composition of the set of important vertebrae. The mean Gini importance for each vertebra level is reported. d Comparison of the receiver operating characteristic (ROC) curves of each individual feature class and with the selected combined features.

Fig. 4

Texture analysis using 3D local binary pattern (3D LBP). The procedure comprised the read-out of the intensity values around a circle centered on the pixel of interest in a binary fashion. If the surrounding pixel value is bigger than the central pixel, it gets the value of 1 and otherwise 0. Then clustering is used on the feature vector. Representatives in visualizing the differences in local binary patterns of L1 using 2 and 3 clusters (k) between a healthy 74-year-old female (noFX) and 73-year-old female from the fracture cohort (FX)