Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Florian Dubost; Benjamin Collery; Antonin Renaudier; Axel Roc; Nicolas Posocco; Wiro Niessen; Marleen de Bruijne

doi:10.1007/978-3-030-39752-4_10

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Florian Dubost^*, Benjamin Collery, Antonin Renaudier, Axel Roc, Nicolas Posocco, Wiro Niessen, Marleen de Bruijne

^*Corresponding author for this work

ImPhys/Computational Imaging

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

7 Citations (Scopus)

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’s test set, we obtained an average symmetric mean absolute percentage error of 22.96.

Original language	English
Title of host publication	Computational Methods and Clinical Applications for Spine Imaging - 6th International Workshop and Challenge, CSI 2019, Proceedings
Editors	Yunliang Cai, Liansheng Wang, Michel Audette, Guoyan Zheng, Shuo Li
Publisher	SpringerOpen
Pages	88-94
ISBN (Print)	9783030397517
DOIs	https://doi.org/10.1007/978-3-030-39752-4_10
Publication status	Published - 2020
Event	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 Duration: 17 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11963 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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
Country/Territory	China
City	Shenzhen
Period	17/10/19 → 17/10/19

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10.1007/978-3-030-39752-4_10

Cite this

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). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11963 LNCS). SpringerOpen. https://doi.org/10.1007/978-3-030-39752-4_10

Dubost, Florian ; Collery, Benjamin ; Renaudier, Antonin et al. / 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. SpringerOpen, 2020. pp. 88-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{04a30aa1e20f45c8bd409589b671fb67,

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 = "SpringerOpen",

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 - 6th International Workshop and Challenge, CSI 2019, Proceedings",

address = "United Kingdom",

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",

}

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11963 LNCS, SpringerOpen, 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, 17/10/19. https://doi.org/10.1007/978-3-030-39752-4_10

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. / Dubost, Florian; Collery, Benjamin; Renaudier, Antonin et al.
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. SpringerOpen, 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/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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.

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U2 - 10.1007/978-3-030-39752-4_10

DO - 10.1007/978-3-030-39752-4_10

M3 - Conference contribution

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 - 6th International Workshop and Challenge, CSI 2019, Proceedings

A2 - Cai, Yunliang

A2 - Wang, Liansheng

A2 - Audette, Michel

A2 - Zheng, Guoyan

A2 - Li, Shuo

PB - SpringerOpen

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 -

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. SpringerOpen. 2020. p. 88-94. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39752-4_10

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

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