A multi-center milestone study of clinical vertebral CT segmentation. Yao, J., Burns, J., E., Forsberg, D., Seitel, A., Rasoulian, A., Abolmaesumi, P., Hammernik, K., Urschler, M., Ibragimov, B., Korez, R., Vrtovec, T., Castro-Mateos, I., Pozo, J., M., Frangi, A., F., Summers, R., M., & Li, S. Computerized Medical Imaging and Graphics, 49:16-28, 4, 2016. ![A multi-center milestone study of clinical vertebral CT segmentation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.
@article{
title = {A multi-center milestone study of clinical vertebral CT segmentation},
type = {article},
year = {2016},
pages = {16-28},
volume = {49},
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month = {4},
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created = {2017-12-02T19:49:53.592Z},
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last_modified = {2019-11-08T01:39:54.087Z},
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abstract = {A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.},
bibtype = {article},
author = {Yao, Jianhua and Burns, Joseph E. and Forsberg, Daniel and Seitel, Alexander and Rasoulian, Abtin and Abolmaesumi, Purang and Hammernik, Kerstin and Urschler, Martin and Ibragimov, Bulat and Korez, Robert and Vrtovec, Tomaž and Castro-Mateos, Isaac and Pozo, Jose M. and Frangi, Alejandro F. and Summers, Ronald M. and Li, Shuo},
doi = {10.1016/j.compmedimag.2015.12.006},
journal = {Computerized Medical Imaging and Graphics}
}
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