Speaker: Prof. Samuel Kadoury, Ph.D., P.Eng., Polytechnique Montreal, Canada Research Chair in Medical Imaging and Assisted Interventions

ABSTRACT:

Spinal deformities such as adolescent idiopathic scoliosis are complex 3D deformations of the musculoskeletal trunk. For the past two decades, 3D spine reconstructions obtained from diagnostic scans have assisted orthopedists assess the severity of deformations and establish treatment strategies. However, these procedures required significant manual intervention and were not suited for routine clinical practice.  This presentation will expose computational methods recently developed in our lab based on machine learning and statistical analysis to automatically reconstruct the personalized spine geometry from X-rays, classify various deformation patterns in 3D, predict disease progression and perform intra-operative guidance during surgical procedures, with the use of biomechanical simulation models and multi-modal registration. Experiments performed at the CHU Sainte-Justine Hospital on adolescent patients demonstrate the potential clinical benefit of capturing statistical variations in the spine geometry to help diagnose and treat this disease.

BIO:

Samuel Kadoury is an associate professor in the Computer and Software Engineering Department at Polytechnique Montreal and researcher at the University of Montreal Research Hospital Center. He is the director of the Medical Image Computing and Analysis Lab at Polytechnique Montreal and holds the Canada Research Chair in Medical Imaging and Assisted Interventions. He obtained his M.Sc. from McGill University and his Ph.D. in biomedical engineering at the University of Montreal, with his thesis on orthopedic imaging. He completed a post-doctoral fellowship at Ecole Centrale de Paris and was a clinical research scientist for Philips Research at the National Institutes of Health, developing image-guided systems for liver and prostate cancer. Dr. Kadoury has published and presented his work in a number of conferences and journals such as Radiology, ISMRM, IEEE TMI,  Medical Image Analysis, MICCAI and IPCAI, and served as Area Chair for conferences such as MICCAI and CVPR. He has also been granted five international and US patents the field of image-guided interventions and is co-recipient of the NIH merit award and the RSNA Cum Laude Award for his work in artificial intelligence for liver cancer detection.