You are not logged in.

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

Zheng, Guoyan, Chu, Chengwen, Belavý, Daniel L, Ibragimov, Bulat, Korez, Robert, Vrtovec, Tomaz, Hutt, Hugo, Everson, Richard, Meakin, Judith, Andrade, Isabel L, Glocker, Ben, Chen, Hao, Dou, Qi, Heng, Pheng-Ann, Wang, Chunliang, Forsberg, Daniel, Neubert, Ales, Fripp, Jurgen, Urschler, Martin, Stern, Darko, Wimmer, Maria, Novikov, Alexey A, Cheng, Hui, Armbrecht, Gabriele, Felsenberg, Dieter and Li, Shuo 2016, Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge., Medical image analysis, vol. 35, pp. 327-344, doi: 10.1016/j.media.2016.08.005.

Attached Files
Name Description MIMEType Size Downloads

Title Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge.
Author(s) Zheng, Guoyan
Chu, Chengwen
Belavý, Daniel LORCID iD for Belavý, Daniel L orcid.org/0000-0002-9307-832X
Ibragimov, Bulat
Korez, Robert
Vrtovec, Tomaz
Hutt, Hugo
Everson, Richard
Meakin, Judith
Andrade, Isabel L
Glocker, Ben
Chen, Hao
Dou, Qi
Heng, Pheng-Ann
Wang, Chunliang
Forsberg, Daniel
Neubert, Ales
Fripp, Jurgen
Urschler, Martin
Stern, Darko
Wimmer, Maria
Novikov, Alexey A
Cheng, Hui
Armbrecht, Gabriele
Felsenberg, Dieter
Li, Shuo
Journal name Medical image analysis
Volume number 35
Start page 327
End page 344
Total pages 18
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-08-17
ISSN 1361-8423
Keyword(s) Challenge
Evaluation
Intervertebral disc
Localization
MRI
Segmentation
Summary 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.
Language eng
DOI 10.1016/j.media.2016.08.005
Field of Research 110399 Clinical Sciences not elsewhere classified
Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085782

Document type: Journal Article
Collection: School of Exercise and Nutrition Sciences
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 42 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 31 Aug 2016, 09:07:51 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.