Literature DB >> 20009200

Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine.

Darko Stern1, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec.   

Abstract

We propose a completely automated algorithm for the detection of the spinal centreline and the centres of vertebral bodies and intervertebral discs in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging. The developed methods are based on the analysis of the geometry of spinal structures and the characteristics of CT and MR images and were evaluated on 29 CT and 13 MR images of lumbar spine. The overall mean distance between the obtained and the ground truth spinal centrelines and centres of vertebral bodies and intervertebral discs were 1.8 +/- 1.1 mm and 2.8 +/- 1.9 mm, respectively, and no considerable differences were detected among the results for CT, T(1)-weighted MR and T(2)-weighted MR images. The knowledge of the location of the spinal centreline and the centres of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. The proposed method may therefore be used to initialize the techniques for labelling and segmentation of vertebrae.

Mesh:

Year:  2010        PMID: 20009200     DOI: 10.1088/0031-9155/55/1/015

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


  6 in total

1.  Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.

Authors:  Maria Wimmer; David Major; Alexey A Novikov; Katja Bühler
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-07-19       Impact factor: 2.924

Review 2.  On computerized methods for spine analysis in MRI: a systematic review.

Authors:  Marko Rak; Klaus D Tönnies
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-02-09       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.  Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

Authors:  Zhongyi Han; Benzheng Wei; Stephanie Leung; Ilanit Ben Nachum; David Laidley; Shuo Li
Journal:  Neuroinformatics       Date:  2018-10

5.  Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.

Authors:  Bulat Ibragimov; Diego Toesca; Daniel Chang; Albert Koong; Lei Xing
Journal:  Phys Med Biol       Date:  2017-11-10       Impact factor: 3.609

6.  Automatic labeling of vertebral levels using a robust template-based approach.

Authors:  Eugénie Ullmann; Jean François Pelletier Paquette; William E Thong; Julien Cohen-Adad
Journal:  Int J Biomed Imaging       Date:  2014-07-15
  6 in total

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