9 March 2011 Segmentation of vertebral bodies in CT and MR images based on 3D deterministic models
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79620D (2011) https://doi.org/10.1117/12.877365
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
The evaluation of vertebral deformations is of great importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards the computed tomography (CT) and magnetic resonance (MR) imaging techniques, as they can provide a detailed 3D representation of vertebrae, the established methods for the evaluation of vertebral deformations still provide only a two-dimensional (2D) geometrical description. Segmentation of vertebrae in 3D may therefore not only improve their visualization, but also provide reliable and accurate 3D measurements of vertebral deformations. In this paper we propose a method for 3D segmentation of individual vertebral bodies that can be performed in CT and MR images. Initialized with a single point inside the vertebral body, the segmentation is performed by optimizing the parameters of a 3D deterministic model of the vertebral body to achieve the best match of the model to the vertebral body in the image. The performance of the proposed method was evaluated on five CT (40 vertebrae) and five T2-weighted MR (40 vertebrae) spine images, among them five are normal and five are pathological. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images and that the proposed model can describe a variety of vertebral body shapes. The method may be therefore used for initializing whole vertebra segmentation or reliably describing vertebral body deformations.
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Darko Štern, Darko Štern, Tomaž Vrtovec, Tomaž Vrtovec, Franjo Pernuš, Franjo Pernuš, Boštjan Likar, Boštjan Likar, } "Segmentation of vertebral bodies in CT and MR images based on 3D deterministic models", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620D (9 March 2011); doi: 10.1117/12.877365; https://doi.org/10.1117/12.877365
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