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

Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge. Zheng, G., Chu, C., Belavý, D., L., Ibragimov, B., Korez, R., Vrtovec, T., Hutt, H., Everson, R., Meakin, J., Andrade, I., L., Glocker, B., Chen, H., Dou, Q., Heng, P., Wang, C., Forsberg, D., Neubert, A., Fripp, J., Urschler, M., Stern, D., Wimmer, M., Novikov, A., A., Cheng, H., Armbrecht, G., Felsenberg, D., & Li, S. Medical Image Analysis, 35(1):327-344, 1, 2017.

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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.

@article{
 title = {Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge},
 type = {article},
 year = {2017},
 keywords = {Challenge,Evaluation,Intervertebral disc,Localization,MRI,Segmentation},
 pages = {327-344},
 volume = {35},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S1361841516301530},
 month = {1},
 id = {063d9446-66ee-3735-9db7-80c464f0a67b},
 created = {2017-12-02T19:49:53.738Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2019-11-08T01:39:38.203Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zheng2017},
 folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Zheng, Guoyan and Chu, Chengwen and Belavý, Daniel L. and Ibragimov, Bulat and Korez, Robert and Vrtovec, Tomaž and Hutt, Hugo and Everson, Richard and Meakin, Judith and Andrade, Isabel Lŏpez and Glocker, Ben and Chen, Hao and Dou, Qi and Heng, Pheng-Ann and Wang, Chunliang and Forsberg, Daniel and Neubert, Aleš and Fripp, Jurgen and Urschler, Martin and Stern, Darko and Wimmer, Maria and Novikov, Alexey A. and Cheng, Hui and Armbrecht, Gabriele and Felsenberg, Dieter and Li, Shuo},
 doi = {10.1016/j.media.2016.08.005},
 journal = {Medical Image Analysis},
 number = {1}
}

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