TY - GEN
T1 - Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning
AU - Tapp, Austin
AU - Payer, Christian
AU - Schmid, Jérôme
AU - Polanco, Michael
AU - Kumi, Isaac
AU - Bawab, Sebastian
AU - Ringleb, Stacie
AU - St. Remy, Carl
AU - Bennett, James
AU - Kakar, Rumit Singh
AU - Audette, Michel
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
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.
KW - Adolescent idiopathic scoliosis
KW - Enhanced CT visualization
KW - Finite elements
KW - Ligamentous meshing
KW - Patient-specific
KW - Pre-surgical planning
UR - http://www.scopus.com/inward/record.url?scp=85120700657&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90874-4_2
DO - 10.1007/978-3-030-90874-4_2
M3 - Conference paper
AN - SCOPUS:85120700657
SN - 9783030908737
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 23
BT - 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
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
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
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
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 -