Literature DB >> 27567734

Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge.

Guoyan Zheng1, Chengwen Chu2, Daniel L Belavý3, Bulat Ibragimov4, Robert Korez5, Tomaž Vrtovec5, Hugo Hutt6, Richard Everson6, Judith Meakin6, Isabel Lŏpez Andrade7, Ben Glocker7, Hao Chen8, Qi Dou8, Pheng-Ann Heng8, Chunliang Wang9, Daniel Forsberg10, Aleš Neubert11, Jurgen Fripp12, Martin Urschler13, Darko Stern14, Maria Wimmer15, Alexey A Novikov15, Hui Cheng2, Gabriele Armbrecht16, Dieter Felsenberg16, Shuo Li17.   

Abstract

The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8 mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1 mm and a mean Hausdorff distance of 4.3 mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Challenge; Evaluation; Intervertebral disc; Localization; MRI; Segmentation

Mesh:

Year:  2016        PMID: 27567734     DOI: 10.1016/j.media.2016.08.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  17 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Authors:  Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana
Journal:  Acad Radiol       Date:  2019-08-10       Impact factor: 3.173

2.  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

3.  Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation.

Authors:  Rabia Haq; Jérôme Schmid; Roderick Borgie; Joshua Cates; Michel A Audette
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-22

4.  A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.

Authors:  Danis Alukaev; Semen Kiselev; Tamerlan Mustafaev; Ahatov Ainur; Bulat Ibragimov; Tomaž Vrtovec
Journal:  Eur Spine J       Date:  2022-05-21       Impact factor: 2.721

Review 5.  Deep Learning Approaches for Automatic Localization in Medical Images.

Authors:  H Alaskar; A Hussain; B Almaslukh; T Vaiyapuri; Z Sbai; Arun Kumar Dubey
Journal:  Comput Intell Neurosci       Date:  2022-06-29

6.  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

7.  In vivo relationships between lumbar facet joint and intervertebral disc composition and diurnal deformation.

Authors:  Alexander B Oldweiler; John T Martin
Journal:  Clin Biomech (Bristol, Avon)       Date:  2021-07-14       Impact factor: 2.034

8.  Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model.

Authors:  Dominik Gaweł; Paweł Główka; Tomasz Kotwicki; Michał Nowak
Journal:  Biomed Res Int       Date:  2018-04-29       Impact factor: 3.411

9.  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

10.  Lumbar intervertebral disc characterization through quantitative MRI analysis: An automatic voxel-based relaxometry approach.

Authors:  Claudia Iriondo; Valentina Pedoia; Sharmila Majumdar
Journal:  Magn Reson Med       Date:  2020-02-14       Impact factor: 4.668

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