Literature DB >> 31522345

Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data.

Ana Jimenez-Pastor1, Angel Alberich-Bayarri2,3, Belen Fos-Guarinos2, Fabio Garcia-Castro2, David Garcia-Juan2, Ben Glocker4, Luis Marti-Bonmati2,5.   

Abstract

PURPOSE: Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).
MATERIALS AND METHODS: This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.
RESULTS: The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.
CONCLUSION: The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.

Keywords:  Artificial intelligence; Computed tomography; Decision forest; Spine

Mesh:

Year:  2019        PMID: 31522345     DOI: 10.1007/s11547-019-01079-9

Source DB:  PubMed          Journal:  Radiol Med        ISSN: 0033-8362            Impact factor:   3.469


  11 in total

1.  Automated localization and identification of lower spinal anatomy in magnetic resonance images.

Authors:  M P Chwialkowski; P E Shile; D Pfeifer; R W Parkey; R M Peshock
Journal:  Comput Biomed Res       Date:  1991-04

2.  Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model.

Authors:  Jun Ma; Le Lu; Yiqiang Zhan; Xiang Zhou; Marcos Salganicoff; Arun Krishnan
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

3.  Spine detection and labeling using a parts-based graphical model.

Authors:  Stefan Schmidt; Jörg Kappes; Martin Bergtholdt; Vladimir Pekar; Sebastian Dries; Daniel Bystrov; Christoph Schnörr
Journal:  Inf Process Med Imaging       Date:  2007

4.  Automated model-based vertebra detection, identification, and segmentation in CT images.

Authors:  Tobias Klinder; Jörn Ostermann; Matthias Ehm; Astrid Franz; Reinhard Kneser; Cristian Lorenz
Journal:  Med Image Anal       Date:  2009-02-20       Impact factor: 8.545

5.  Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans.

Authors:  Ben Glocker; J Feulner; Antonio Criminisi; D R Haynor; E Konukoglu
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

6.  Vertebral body segmentation in MRI via convex relaxation and distribution matching.

Authors:  Ismail Ben Ayed; Kumaradevan Punithakumar; Rashid Minhas; Kumradvan Rohit Joshi; Gregory J Garvin
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

7.  Vertebrae localization in pathological spine CT via dense classification from sparse annotations.

Authors:  Ben Glocker; Darko Zikic; Ender Konukoglu; David R Haynor; Antonio Criminisi
Journal:  Med Image Comput Comput Assist Interv       Date:  2013

8.  Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information.

Authors:  Haofu Liao; Addisu Mesfin; Jiebo Luo
Journal:  IEEE Trans Med Imaging       Date:  2018-05       Impact factor: 10.048

9.  Square-cut: a segmentation algorithm on the basis of a rectangle shape.

Authors:  Jan Egger; Tina Kapur; Thomas Dukatz; Malgorzata Kolodziej; Dženan Zukić; Bernd Freisleben; Christopher Nimsky
Journal:  PLoS One       Date:  2012-02-21       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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  2 in total

Review 1.  Artificial intelligence in spine care: current applications and future utility.

Authors:  Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolás Barajas; Augustus Rush; Arash J Sayari; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis
Journal:  Eur Spine J       Date:  2022-03-27       Impact factor: 2.721

Review 2.  Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.

Authors:  Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolas Barajas; Alejandro A Espinoza Orías; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis
Journal:  Eur Spine J       Date:  2022-01-27       Impact factor: 2.721

  2 in total

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