Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning

Austin Tapp; Christian Payer; Jérôme Schmid; Michael Polanco; Isaac Kumi; Sebastian Bawab; Stacie Ringleb; Carl St. Remy; James Bennett; Rumit Singh Kakar; Michel Audette

doi:10.1007/978-3-030-90874-4_2

Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning

Austin Tapp^*, Christian Payer, Jérôme Schmid, Michael Polanco, Isaac Kumi, Sebastian Bawab, Stacie Ringleb, Carl St. Remy, James Bennett, Rumit Singh Kakar, Michel Audette

^*Corresponding author for this work

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Finite element (FE) biomechanical studies for adolescent idiopathic scoliosis (AIS) treatments will greatly benefit from utilizing true-scale, patient-specific anatomy that accurately characterizes all tissue properties. This study presents a method to automatically generate patient-specific, FE meshes containing volumetric soft tissues, such as ligaments and cartilage, that are inconspicuous in computed tomography (CT) imaging of AIS patients. Convolutional neural network (CNN) derived vertebrae segmentations, obtained from CT scans, provide a foundation for subsequent elastic deformations of ligamentoskeletal, computer-aided designed (CAD) surface meshes, to ascertain patient-specific anatomy, including soft tissue structures. Patient-specific, ligamentoskeletal meshes are then tetrahedralized for use in FE studies. Dice similarity coefficients of 90% and submillimeter Hausdorff distances demonstrate vertebrae and intervertebral disc fitting accuracy of the automatic methodology. In severe AIS cases, when CNN segmentations fail due to overfitting, a semi-automatic step augments the automatic method. The generated FE meshes can provide the basis for biomechanical simulations seeking to correct AIS through bracing, minimally invasive operations, or patient-specific, surgical procedures, like posterior spinal fusion.

Original language	English
Title of host publication	Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, Jonathan Passerat-Palmbach, Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, Jonathan Passerat-Palmbach
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	13-23
Number of pages	11
ISBN (Print)	9783030908737
DOIs	https://doi.org/10.1007/978-3-030-90874-4_2
Publication status	Published - 2021
Event	10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sept 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12969 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	27/09/21 → 1/10/21

Keywords

Adolescent idiopathic scoliosis
Enhanced CT visualization
Finite elements
Ligamentous meshing
Patient-specific
Pre-surgical planning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-90874-4_2

Cite this

Tapp, A., Payer, C., Schmid, J., Polanco, M., Kumi, I., Bawab, S., Ringleb, S., St. Remy, C., Bennett, J., Kakar, R. S., & Audette, M. (2021). Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning. In C. Oyarzun Laura, M. J. Cardoso, M. Rosen-Zvi, G. Kaissis, M. G. Linguraru, R. Shekhar, S. Wesarg, M. Erdt, K. Drechsler, Y. Chen, S. Albarqouni, S. Bakas, B. Landman, N. Rieke, H. Roth, X. Li, D. Xu, M. Gabrani, E. Konukoglu, M. Guindy, D. Rueckert, A. Ziller, D. Usynin, J. Passerat-Palmbach, C. Oyarzun Laura, M. J. Cardoso, M. Rosen-Zvi, G. Kaissis, M. G. Linguraru, R. Shekhar, S. Wesarg, M. Erdt, K. Drechsler, Y. Chen, S. Albarqouni, S. Bakas, B. Landman, N. Rieke, H. Roth, X. Li, D. Xu, M. Gabrani, E. Konukoglu, M. Guindy, D. Rueckert, A. Ziller, D. Usynin, ... J. Passerat-Palmbach (Eds.), Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 13-23). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12969 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90874-4_2

Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning. / Tapp, Austin; Payer, Christian; Schmid, Jérôme et al.
Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Cristina Oyarzun Laura; M. Jorge Cardoso; Michal Rosen-Zvi; Georgios Kaissis; Marius George Linguraru; Raj Shekhar; Stefan Wesarg; Marius Erdt; Klaus Drechsler; Yufei Chen; Shadi Albarqouni; Spyridon Bakas; Bennett Landman; Nicola Rieke; Holger Roth; Xiaoxiao Li; Daguang Xu; Maria Gabrani; Ender Konukoglu; Michal Guindy; Daniel Rueckert; Alexander Ziller; Dmitrii Usynin; Jonathan Passerat-Palmbach; Cristina Oyarzun Laura; M. Jorge Cardoso; Michal Rosen-Zvi; Georgios Kaissis; Marius George Linguraru; Raj Shekhar; Stefan Wesarg; Marius Erdt; Klaus Drechsler; Yufei Chen; Shadi Albarqouni; Spyridon Bakas; Bennett Landman; Nicola Rieke; Holger Roth; Xiaoxiao Li; Daguang Xu; Maria Gabrani; Ender Konukoglu; Michal Guindy; Daniel Rueckert; Alexander Ziller; Dmitrii Usynin; Jonathan Passerat-Palmbach. Springer Science and Business Media Deutschland GmbH, 2021. p. 13-23 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12969 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Tapp, A, Payer, C, Schmid, J, Polanco, M, Kumi, I, Bawab, S, Ringleb, S, St. Remy, C, Bennett, J, Kakar, RS & Audette, M 2021, Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning. in C Oyarzun Laura, MJ Cardoso, M Rosen-Zvi, G Kaissis, MG Linguraru, R Shekhar, S Wesarg, M Erdt, K Drechsler, Y Chen, S Albarqouni, S Bakas, B Landman, N Rieke, H Roth, X Li, D Xu, M Gabrani, E Konukoglu, M Guindy, D Rueckert, A Ziller, D Usynin, J Passerat-Palmbach, C Oyarzun Laura, MJ Cardoso, M Rosen-Zvi, G Kaissis, MG Linguraru, R Shekhar, S Wesarg, M Erdt, K Drechsler, Y Chen, S Albarqouni, S Bakas, B Landman, N Rieke, H Roth, X Li, D Xu, M Gabrani, E Konukoglu, M Guindy, D Rueckert, A Ziller, D Usynin & J Passerat-Palmbach (eds), Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12969 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 13-23, 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-90874-4_2

Tapp A, Payer C, Schmid J, Polanco M, Kumi I, Bawab S et al. Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning. In Oyarzun Laura C, Cardoso MJ, Rosen-Zvi M, Kaissis G, Linguraru MG, Shekhar R, Wesarg S, Erdt M, Drechsler K, Chen Y, Albarqouni S, Bakas S, Landman B, Rieke N, Roth H, Li X, Xu D, Gabrani M, Konukoglu E, Guindy M, Rueckert D, Ziller A, Usynin D, Passerat-Palmbach J, Oyarzun Laura C, Cardoso MJ, Rosen-Zvi M, Kaissis G, Linguraru MG, Shekhar R, Wesarg S, Erdt M, Drechsler K, Chen Y, Albarqouni S, Bakas S, Landman B, Rieke N, Roth H, Li X, Xu D, Gabrani M, Konukoglu E, Guindy M, Rueckert D, Ziller A, Usynin D, Passerat-Palmbach J, editors, Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 13-23. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-90874-4_2

Tapp, Austin ; Payer, Christian ; Schmid, Jérôme et al. / Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Cristina Oyarzun Laura ; M. Jorge Cardoso ; Michal Rosen-Zvi ; Georgios Kaissis ; Marius George Linguraru ; Raj Shekhar ; Stefan Wesarg ; Marius Erdt ; Klaus Drechsler ; Yufei Chen ; Shadi Albarqouni ; Spyridon Bakas ; Bennett Landman ; Nicola Rieke ; Holger Roth ; Xiaoxiao Li ; Daguang Xu ; Maria Gabrani ; Ender Konukoglu ; Michal Guindy ; Daniel Rueckert ; Alexander Ziller ; Dmitrii Usynin ; Jonathan Passerat-Palmbach ; Cristina Oyarzun Laura ; M. Jorge Cardoso ; Michal Rosen-Zvi ; Georgios Kaissis ; Marius George Linguraru ; Raj Shekhar ; Stefan Wesarg ; Marius Erdt ; Klaus Drechsler ; Yufei Chen ; Shadi Albarqouni ; Spyridon Bakas ; Bennett Landman ; Nicola Rieke ; Holger Roth ; Xiaoxiao Li ; Daguang Xu ; Maria Gabrani ; Ender Konukoglu ; Michal Guindy ; Daniel Rueckert ; Alexander Ziller ; Dmitrii Usynin ; Jonathan Passerat-Palmbach. Springer Science and Business Media Deutschland GmbH, 2021. pp. 13-23 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning",

abstract = "Finite element (FE) biomechanical studies for adolescent idiopathic scoliosis (AIS) treatments will greatly benefit from utilizing true-scale, patient-specific anatomy that accurately characterizes all tissue properties. This study presents a method to automatically generate patient-specific, FE meshes containing volumetric soft tissues, such as ligaments and cartilage, that are inconspicuous in computed tomography (CT) imaging of AIS patients. Convolutional neural network (CNN) derived vertebrae segmentations, obtained from CT scans, provide a foundation for subsequent elastic deformations of ligamentoskeletal, computer-aided designed (CAD) surface meshes, to ascertain patient-specific anatomy, including soft tissue structures. Patient-specific, ligamentoskeletal meshes are then tetrahedralized for use in FE studies. Dice similarity coefficients of 90% and submillimeter Hausdorff distances demonstrate vertebrae and intervertebral disc fitting accuracy of the automatic methodology. In severe AIS cases, when CNN segmentations fail due to overfitting, a semi-automatic step augments the automatic method. The generated FE meshes can provide the basis for biomechanical simulations seeking to correct AIS through bracing, minimally invasive operations, or patient-specific, surgical procedures, like posterior spinal fusion.",

keywords = "Adolescent idiopathic scoliosis, Enhanced CT visualization, Finite elements, Ligamentous meshing, Patient-specific, Pre-surgical planning",

author = "Austin Tapp and Christian Payer and J{\'e}r{\^o}me Schmid and Michael Polanco and Isaac Kumi and Sebastian Bawab and Stacie Ringleb and {St. Remy}, Carl and James Bennett and Kakar, {Rumit Singh} and Michel Audette",

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AU - Tapp, Austin

AU - Payer, Christian

AU - Schmid, Jérôme

AU - Polanco, Michael

AU - Kumi, Isaac

AU - Bawab, Sebastian

AU - Ringleb, Stacie

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AB - Finite element (FE) biomechanical studies for adolescent idiopathic scoliosis (AIS) treatments will greatly benefit from utilizing true-scale, patient-specific anatomy that accurately characterizes all tissue properties. This study presents a method to automatically generate patient-specific, FE meshes containing volumetric soft tissues, such as ligaments and cartilage, that are inconspicuous in computed tomography (CT) imaging of AIS patients. Convolutional neural network (CNN) derived vertebrae segmentations, obtained from CT scans, provide a foundation for subsequent elastic deformations of ligamentoskeletal, computer-aided designed (CAD) surface meshes, to ascertain patient-specific anatomy, including soft tissue structures. Patient-specific, ligamentoskeletal meshes are then tetrahedralized for use in FE studies. Dice similarity coefficients of 90% and submillimeter Hausdorff distances demonstrate vertebrae and intervertebral disc fitting accuracy of the automatic methodology. In severe AIS cases, when CNN segmentations fail due to overfitting, a semi-automatic step augments the automatic method. The generated FE meshes can provide the basis for biomechanical simulations seeking to correct AIS through bracing, minimally invasive operations, or patient-specific, surgical procedures, like posterior spinal fusion.

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KW - Enhanced CT visualization

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KW - Ligamentous meshing

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A2 - Kaissis, Georgios

A2 - Linguraru, Marius George

A2 - Shekhar, Raj

A2 - Wesarg, Stefan

A2 - Erdt, Marius

A2 - Drechsler, Klaus

A2 - Chen, Yufei

A2 - Albarqouni, Shadi

A2 - Bakas, Spyridon

A2 - Landman, Bennett

A2 - Rieke, Nicola

A2 - Roth, Holger

A2 - Li, Xiaoxiao

A2 - Xu, Daguang

A2 - Gabrani, Maria

A2 - Konukoglu, Ender

A2 - Guindy, Michal

A2 - Rueckert, Daniel

A2 - Ziller, Alexander

A2 - Usynin, Dmitrii

A2 - Passerat-Palmbach, Jonathan

A2 - Oyarzun Laura, Cristina

A2 - Cardoso, M. Jorge

A2 - Rosen-Zvi, Michal

A2 - Kaissis, Georgios

A2 - Linguraru, Marius George

A2 - Shekhar, Raj

A2 - Wesarg, Stefan

A2 - Erdt, Marius

A2 - Drechsler, Klaus

A2 - Chen, Yufei

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A2 - Roth, Holger

A2 - Li, Xiaoxiao

A2 - Xu, Daguang

A2 - Gabrani, Maria

A2 - Konukoglu, Ender

A2 - Guindy, Michal

A2 - Rueckert, Daniel

A2 - Ziller, Alexander

A2 - Usynin, Dmitrii

A2 - Passerat-Palmbach, Jonathan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

Y2 - 27 September 2021 through 1 October 2021

ER -

Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning

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