Quantitative vertebral morphometry in 3D

Darko Štern; Vesna Njagulj; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec

doi:10.1117/12.2003646

13 March 2013 Quantitative vertebral morphometry in 3D

Darko Štern, Vesna Njagulj, Boštjan Likar, Franjo Pernuš, Tomaž Vrtovec

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86690F (2013) https://doi.org/10.1117/12.2003646
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Identification of vertebral deformations in two dimensions (2D) is a challenging task due to the projective nature of radiographic images and natural anatomical variability of vertebrae. By generating detailed three-dimensional (3D) anatomical images, computed tomography (CT) enables accurate measurement of vertebral deformations. We present a novel approach to quantitative vertebral morphometry (QVM) based on parametric modeling of the vertebral body shape in 3D. A detailed 3D representation of the vertebral body shape is obtained by automatically aligning a parametric 3D model to vertebral bodies in CT images. The parameters of the 3D model describe clinically meaningful morphometric vertebral body features, and QVM in 3D is performed by comparing the parameters to their statistical values. By applying statistical classification analysis, thresholds and parameters that best discriminate between normal and fractured vertebral bodies are determined. The proposed QVM in 3D was applied to 454 normal and 228 fractured vertebral bodies, yielding classification sensitivity of 92:5% at 7:5% specificity, with corresponding accuracy of 92:5% and precision of 86:1%. The 3D shape parameters that provided the best separation between normal and fractured vertebral bodies were the vertebral body height, and the inclination and concavity of both vertebral endplates. The described QVM in 3D is able to efficiently discriminate between normal and fractured vertebral bodies, and identify morphological cases (wedge, (bi)concavity, crush) and grades (1, 2, 3) of vertebral body deformations. It may be therefore valuable for diagnosing and predicting vertebral fractures in patients who are at risk of osteoporosis.

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Darko Štern, Vesna Njagulj, Boštjan Likar, Franjo Pernuš, and Tomaž Vrtovec "Quantitative vertebral morphometry in 3D", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690F (13 March 2013); https://doi.org/10.1117/12.2003646

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KEYWORDS

3D modeling

3D image processing

Computed tomography

3D metrology

Statistical analysis

Statistical modeling

Bone

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