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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


Recent publications
Journal article
Parent, F., Gérard, M., Monet, F., Loranger, S., Soulez, G., Kashyap, R. & Kadoury, S. (2019). Intra-arterial Image Guidance with Optical Frequency Domain Reflectometry Shape Sensing. IEEE Transactions on Medical Imaging, 38(2), 482-492. Retrieved from
Conference paper
Boucher, M.-A., Watts, N., Gremillet, F., Legare, P. & Kadoury, S. (2018). Asymmetry quantification from reflectance images of orthotic patients using structural similarity metrics. Paper presented at the Medical Imaging : Computer-Aided Diagnosis, Houston, TX, United states. Retrieved from
Conference paper
Lessard, S., Plantefeve, R., Michaud, F., Huet, C., Soulez, G. & Kadoury, S. (2018). Blood-flow estimation in the hepatic arteries based on 3D/2D angiography registration. Paper presented at the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT 2018) and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (LABELS 2018), Granada, Spain (pp. 3-10). Retrieved from
Journal article
Nozadi, S.H. & Kadoury, S. (2018). Classification of alzheimer's and MCI patients from semantically parcelled PET Images: a comparison between AV45 and FDG-PET. International Journal of Biomedical Imaging, 2018, 13 pages. Retrieved from

Supervision at Polytechnique


  • 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
  • Master's Thesis (7)

    • Boucher, M.-A. (2018). Volumétrie des ventricules latéraux chez le nouveau-né par segmentation automatique d'échographies 3D (Master's Thesis, École Polytechnique de Montréal). Retrieved from
    • 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
    • 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
    • Mandal, K.K. (2016). Intra-Operative Needle Tracking Using Optical Shape Sensing Technology (Master's Thesis, École Polytechnique de Montréal). Retrieved from
    • 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
    • Thong, W. (2015). Apprentissage de représentations pour la classification d'images biomédicales (Master's Thesis, École Polytechnique de Montréal). Retrieved from
    • 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

News about Samuel Kadoury

NEWS | October 15, 2018
Four Canada Research Chairs at Polytechnique Montréal are advancing knowledge in several areas | Read
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.