期刊
MEDICAL IMAGE ANALYSIS
卷 35, 期 -, 页码 327-344出版社
ELSEVIER
DOI: 10.1016/j.media.2016.08.005
关键词
Intervertebral disc; MRI; Localization; Segmentation; Challenge; Evaluation
类别
资金
- Swiss National Science Foundation [205321 - 157207/1]
- European Space Agency [14431/02/NL/SH2]
- German Aerospace Center (DLR) [50WB0720]
- Charite University Medical School Berlin
- Slovenian Research Agency [P2 - 0232, J2 - 5473, J7 - 6781, J7 - 7118]
- Dunhill Medical Trust [R401/0215]
- Fundacion Barrie
- Swedish Innovation Agency (VINNOVA) [2014-01422]
- Hong Kong RGC Fund [CUHK 412513]
- province of Styria [ABT08-22-T-7/2013-13]
- Austrian Science Fund (FWF) [P28078-N33]
- Australian Research Council's linkage project funding scheme [LP100200422]
- competence center VRVis within the scope of COMET [843272]
- Vinnova [2014-01422] Funding Source: Vinnova
- Swiss National Science Foundation (SNF) [205321_157207] Funding Source: Swiss National Science Foundation (SNF)
- Austrian Science Fund (FWF) [P28078] Funding Source: Austrian Science Fund (FWF)
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. (C) 2016 Elsevier B.V. All rights reserved.
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