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Samuel Kadoury
B.Ing. (Poly), M.Ing. (McGill), Ph.D. (Montréal), Postdoc (INRIA)

Research interests and affiliations

Research interests

The research interests of professor Kadoury pertain to the development of novel methods and systems in medical imaging related to a broad range of clinical applications, including neurology, cardiovascular interventions, orthopaedics and interventional oncology. These new technologies in image registration (temporal, mono and multimodality), segmentation, organ atlas conception, statistical shape modeling, classification, and minimally invasive treatments are validated through clinical trials, both with adult and paediatric populations.

  • Medical imaging
  • Computer vision
  • Pattern recognition and shape modeling
  • Interventional navigation systems
  • Multimodal image fusion
  • Machine learning
Expertise type(s) (NSERC subjects)
  • 1901 Biomedical technology
  • 2603 Computer vision
  • 2604 Intelligent systems applications
  • 2605 Pattern analysis and machine intelligence
  • 2708 Image and video processing

Publications

Recent publications
Conference paper
Petkovi, U., Korez, R., Parent, S., Kadoury, S. & Vrtovec, T. (2018). 3D Cobb angle measurements from scoliotic mesh models with varying face-vertex density. Paper presented at the 5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging (MSKI 2017), Quebec City, QC (pp. 48-58). Retrieved from https://doi.org/10.1007/978-3-319-74113-0_5
Journal article
Poulin, E., Boudam, K., Pinter, C., Kadoury, S., Lasso, A., Fichtinger, G. & Ménard, C. (2018). Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy. Brachytherapy, 17(2), 283-290. Retrieved from https://doi.org/10.1016/j.brachy.2017.11.018
Journal article
Mandal, K.K., Parent, F., Kashyap, R., Martel, S. & Kadoury, S. (2017). Assessment of the accuracy of optical shape sensing for needle tracking interventions. Journal of Medical Devices, 11(3), 7 pages. Retrieved from https://doi.org/10.1115/1.4036338
Journal article
Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., Kadoury, S. & Tang, A. (2017). Deep Learning: A Primer for Radiologists. RadioGraphics, 37(7), 2113-2131. Retrieved from https://doi.org/10.1148/rg.2017170077

Supervision at Polytechnique

COMPLETED

  • Ph.D. Thesis (1)

    • Shakeri, M. (2016). Analysis of sub-cortical morphology in benign epilepsy with centrotemporal spikes (Ph.D. Thesis, École Polytechnique de Montréal). Retrieved from https://publications.polymtl.ca/2453/
  • Master's Thesis (6)

    • Nozadi, S.H. (2017). Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Longitudinal FDG-PET Images (Master's Thesis, École Polytechnique de Montréal). Retrieved from http://publications.polymtl.ca/2586/
    • Gérard, M. (2016). Modélisation 3D des artères hépatiques et visualisation par fusion d'imagerie par résonance magnétique et d'imagerie ultrasonore (Master's Thesis, École Polytechnique de Montréal). Retrieved from http://publications.polymtl.ca/2390/
    • Mandal, K.K. (2016). Intra-Operative Needle Tracking Using Optical Shape Sensing Technology (Master's Thesis, École Polytechnique de Montréal). Retrieved from http://publications.polymtl.ca/2090/
    • Parent, F. (2016). Reconstruction 3D de la forme d'aiguilles chirurgicales en utilisant la réflectométrie fréquentielle dans des fibres optiques (Master's Thesis, École Polytechnique de Montréal). Retrieved from http://publications.polymtl.ca/2126/
    • Thong, W. (2015). Apprentissage de représentations pour la classification d'images biomédicales (Master's Thesis, École Polytechnique de Montréal). Retrieved from http://publications.polymtl.ca/1842/
    • De Leener, B. (2014). Segmentation automatique de la moelle épinière sur des images de résonance magnétique par propagation de modèles déformables (Master's Thesis, École Polytechnique de Montréal). Retrieved from http://publications.polymtl.ca/1471

News about Samuel Kadoury

NEWS | February 22, 2018
Deep neural networks identifies tumours | Read

Press review about Samuel Kadoury

November 18, 2017, Le Devoir, Les avancées de l’intelligence artificielle s’apprêtent à bouleverser la pratique médicale Dans son laboratoire, Samuel Kadoury, professeur agrégé au Département de génie informatique et génie logiciel à Polytechnique Montréal, travaille à entraîner des logiciels, dont un capable de déterminer si de jeunes patients atteints de scoliose auront besoin ou pas d'une grande chirurgie.
November 23, 2016, Le médecin du Québec, Médecine et intelligence artificielle : un nouveau monde de possibilités Mentions de Christopher Pal et de Samuel Kadoury, professeurs agrégés au Département de génie informatique et génie logiciel de Polytechnique Montréal.