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

Guoyan Zheng*, Chengwen Chu, Daniel L. Belavý, Bulat Ibragimov, Robert Korez, Tomaž Vrtovec, Hugo Hutt, Richard Everson, Judith Meakin, Isabel Lŏpez Andrade, Ben Glocker, Hao Chen, Qi Dou, Pheng Ann Heng, Chunliang Wang, Daniel Forsberg, Aleš Neubert, Jürgen Fripp, Martin Urschler, Darko SternMaria Wimmer, Alexey A. Novikov, Hui Cheng, Gabriele Armbrecht, Dieter Felsenberg, Shuo Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)327-344
Number of pages18
JournalMedical Image Analysis
Publication statusPublished - 1 Jan 2017


  • Challenge
  • Evaluation
  • Intervertebral disc
  • Localization
  • MRI
  • Segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing


  • BioTechMed-Graz


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