Literature DB >> 22080628

Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images.

Darko Stern1, Boštjan Likar, Franjo Pernuš, Tomaž Vrtovec.   

Abstract

Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T(2)-weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.

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Year:  2011        PMID: 22080628     DOI: 10.1088/0031-9155/56/23/011

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  11 in total

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2.  Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images.

Authors:  Shuang Liu; Yiting Xie; Anthony P Reeves
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-11-11       Impact factor: 2.924

3.  Quantitative vertebral morphometry based on parametric modeling of vertebral bodies in 3D.

Authors:  D Stern; V Njagulj; B Likar; F Pernuš; T Vrtovec
Journal:  Osteoporos Int       Date:  2012-07-24       Impact factor: 4.507

4.  A multi-center milestone study of clinical vertebral CT segmentation.

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Journal:  Comput Med Imaging Graph       Date:  2016-01-02       Impact factor: 4.790

5.  Minimum cement volume for vertebroplasty.

Authors:  David Martinčič; Miha Brojan; Franc Kosel; Darko Štern; Tomaž Vrtovec; Vane Antolič; Rok Vengust
Journal:  Int Orthop       Date:  2014-12-12       Impact factor: 3.075

6.  Improved precision of syndesmophyte measurement for the evaluation of ankylosing spondylitis using CT: a phantom and patient study.

Authors:  Sovira Tan; Jianhua Yao; Lawrence Yao; Michael M Ward
Journal:  Phys Med Biol       Date:  2012-07-02       Impact factor: 3.609

7.  The value of radiographic indexes in the diagnosis of discogenic low back pain: a retrospective analysis of imaging results.

Authors:  Jian Song; Hong-Li Wang; Xiao-Sheng Ma; Xin-Lei Xia; Fei-Zhou Lu; Chao-Jun Zheng; Jian-Yuan Jiang
Journal:  Oncotarget       Date:  2017-06-27

8.  Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application.

Authors:  Jan Egger; Christopher Nimsky; Xiaojun Chen
Journal:  SAGE Open Med       Date:  2017-11-13

9.  Predicting spinal profile using 3D non-contact surface scanning: Changes in surface topography as a predictor of internal spinal alignment.

Authors:  J Paige Little; Lionel Rayward; Mark J Pearcy; Maree T Izatt; Daniel Green; Robert D Labrom; Geoffrey N Askin
Journal:  PLoS One       Date:  2019-09-26       Impact factor: 3.240

10.  Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.

Authors:  Chengwen Chu; Daniel L Belavý; Gabriele Armbrecht; Martin Bansmann; Dieter Felsenberg; Guoyan Zheng
Journal:  PLoS One       Date:  2015-11-23       Impact factor: 3.240

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