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Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. / Dubost, Florian; Collery, Benjamin; Renaudier, Antonin; Roc, Axel; Posocco, Nicolas; Niessen, Wiro; de Bruijne, Marleen.
Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings. ed. / Yunliang Cai; Liansheng Wang; Michel Audette; Guoyan Zheng; Shuo Li. Springer VS, 2020. p. 88-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11963 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Dubost, F, Collery, B, Renaudier, A, Roc, A, Posocco, N, Niessen, W
& de Bruijne, M 2020,
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. in Y Cai, L Wang, M Audette, G Zheng & S Li (eds),
Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings. Springer VS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11963 LNCS, pp. 88-94, 6th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China,
17/10/2019.
https://doi.org/10.1007/978-3-030-39752-4_10APA
Dubost, F., Collery, B., Renaudier, A., Roc, A., Posocco, N., Niessen, W.
, & de Bruijne, M. (2020).
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. In Y. Cai, L. Wang, M. Audette, G. Zheng, & S. Li (Eds.),
Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings (pp. 88-94). Springer VS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11963 LNCS
https://doi.org/10.1007/978-3-030-39752-4_10Vancouver
Dubost F, Collery B, Renaudier A, Roc A, Posocco N, Niessen W et al.
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. In Cai Y, Wang L, Audette M, Zheng G, Li S, editors, Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings. Springer VS. 2020. p. 88-94. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11963 LNCS).
https://doi.org/10.1007/978-3-030-39752-4_10Author
Dubost, Florian ; Collery, Benjamin ; Renaudier, Antonin ; Roc, Axel ; Posocco, Nicolas ; Niessen, Wiro ; de Bruijne, Marleen. / Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings. editor / Yunliang Cai ; Liansheng Wang ; Michel Audette ; Guoyan Zheng ; Shuo Li. Springer VS, 2020. pp. 88-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11963 LNCS).
Bibtex
@inproceedings{cdf9ecc060544fafbc3265fc6239274b,
title = "Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks",
abstract = "Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge “Accurate Automated Spinal Curvature Estimation, MICCAI 2019” (100 scans). On the challenge{\textquoteright}s test set, we obtained an average symmetric mean absolute percentage error of 22.96.",
author = "Florian Dubost and Benjamin Collery and Antonin Renaudier and Axel Roc and Nicolas Posocco and Wiro Niessen and {de Bruijne}, Marleen",
year = "2020",
doi = "10.1007/978-3-030-39752-4_10",
language = "English",
isbn = "9783030397517",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer VS",
pages = "88--94",
editor = "Yunliang Cai and Liansheng Wang and Michel Audette and Guoyan Zheng and Shuo Li",
booktitle = "Computational Methods and Clinical Applications for Spine Imaging",
note = "6th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",
}
RIS
TY - GEN
T1 - Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
AU - Dubost, Florian
AU - Collery, Benjamin
AU - Renaudier, Antonin
AU - Roc, Axel
AU - Posocco, Nicolas
AU - Niessen, Wiro
AU - de Bruijne, Marleen
PY - 2020
Y1 - 2020
N2 - Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge “Accurate Automated Spinal Curvature Estimation, MICCAI 2019” (100 scans). On the challenge’s test set, we obtained an average symmetric mean absolute percentage error of 22.96.
AB - Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge “Accurate Automated Spinal Curvature Estimation, MICCAI 2019” (100 scans). On the challenge’s test set, we obtained an average symmetric mean absolute percentage error of 22.96.
UR - http://www.scopus.com/inward/record.url?scp=85080888971&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39752-4_10
DO - 10.1007/978-3-030-39752-4_10
M3 - Article in proceedings
AN - SCOPUS:85080888971
SN - 9783030397517
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 88
EP - 94
BT - Computational Methods and Clinical Applications for Spine Imaging
A2 - Cai, Yunliang
A2 - Wang, Liansheng
A2 - Audette, Michel
A2 - Zheng, Guoyan
A2 - Li, Shuo
PB - Springer VS
T2 - 6th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -
