Valentinitsch, A; Trebeschi, S; Kaesmacher, Johannes; Lorenz, C; Löffler, M T; Zimmer, C; Baum, T; Kirschke, J S (2019). Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporosis international, 30(6), pp. 1275-1285. Springer-Verlag 10.1007/s00198-019-04910-1
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10.1007_s00198-019-04910-1_kaesmacher.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial (CC-BY-NC). Download (2MB) | Preview |
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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology 04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology |
UniBE Contributor: |
Kaesmacher, Johannes |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
0937-941X |
Publisher: |
Springer-Verlag |
Language: |
English |
Submitter: |
Maria de Fatima Henriques Bernardo |
Date Deposited: |
20 May 2019 15:37 |
Last Modified: |
05 Dec 2022 15:27 |
Publisher DOI: |
10.1007/s00198-019-04910-1 |
PubMed ID: |
30830261 |
Uncontrolled Keywords: |
BMD Machine learning Opportunistic screening Osteoporosis Quantitative computed tomography Random forest model Texture analysis Vertebral fractures |
BORIS DOI: |
10.7892/boris.128508 |
URI: |
https://boris.unibe.ch/id/eprint/128508 |
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