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 af dette arbejde

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

7 Citationer (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.

Originalsprog	Engelsk
Titel	Computational Methods and Clinical Applications for Spine Imaging : 6th International Workshop and Challenge, CSI 2019, Proceedings
Redaktører	Yunliang Cai, Liansheng Wang, Michel Audette, Guoyan Zheng, Shuo Li
Antal sider	7
Forlag	Springer VS
Publikationsdato	2020
Sider	88-94
ISBN (Trykt)	9783030397517
DOI	https://doi.org/10.1007/978-3-030-39752-4_10
Status	Udgivet - 2020
Begivenhed	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, Kina Varighed: 17 okt. 2019 → 17 okt. 2019

Konference

Konference	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
Land/Område	Kina
By	Shenzhen
Periode	17/10/2019 → 17/10/2019

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	11963 LNCS
ISSN	0302-9743

Adgang til dokumentet

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

http://arxiv.org/pdf/1911.01126

Citationsformater

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. I Y. Cai, L. Wang, M. Audette, G. Zheng, & S. Li (red.), Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings (s. 88-94). Springer VS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11963 LNCS 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; 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. red. / Yunliang Cai; Liansheng Wang; Michel Audette; Guoyan Zheng; Shuo Li. Springer VS, 2020. s. 88-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11963 LNCS).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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. i Y Cai, L Wang, M Audette, G Zheng & S Li (red), 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), bind 11963 LNCS, s. 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, Kina, 17/10/2019. https://doi.org/10.1007/978-3-030-39752-4_10

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. I Cai Y, Wang L, Audette M, Zheng G, Li S, red., Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings. Springer VS. 2020. s. 88-94. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11963 LNCS). https://doi.org/10.1007/978-3-030-39752-4_10

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. red. / Yunliang Cai ; Liansheng Wang ; Michel Audette ; Guoyan Zheng ; Shuo Li. Springer VS, 2020. s. 88-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11963 LNCS).

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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.",

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AU - Posocco, Nicolas

AU - Niessen, Wiro

AU - de Bruijne, Marleen

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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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