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Lama Séoud
Dipl. Ing., M.A.Sc., Ph.D.

Research interests and affiliations

Research interests
  • 3D imaging and analysis
  • Human motion analysis
  • Medical image computing
  • Computer vision
  • Machine learning
Expertise type(s) (NSERC subjects)
  • 1901 Biomedical technology
  • 2603 Computer vision
  • 2605 Pattern analysis and machine intelligence
  • 2708 Image and video processing


Recent publications


After her PhD in 2012, Lama Seoud completed a postdoctoral fellowship in industry at Diagnos Inc, which she later joined as a researcher. She worked on computer-aided diagnosis of retinal pathologies. In 2016, she joined the computer vision and computer graphics research team at the National Research Council of Canada (NRC) in Ottawa. She worked for 3 years on the integration of deep learning with the acquisition and analysis of 3D data sequences, including human subjects in motion. Based on these experiences, her current research program at Polytechnique is in the areas of computer vision (capture and analysis of human pose and motion for medical, multimedia and industrial applications) and computational medical imaging (computer-aided diagnosis). Through her projects, she collaborates closely with clinicians (intensivists, pathologists, physiotherapists, surgeons and orthopedists), industrial partners and artists (circus and music).


INF1010 - Object-oriented programming

INF8725 – Signal and image processing

INF8801A - Multimedia applications


  • Ph.D. in biomedical engineering, Polytechnique Montreal, Canada
  • Master in biomedical engineering, Polytechnique Montreal, Canada
  • Diploma in biomedical engineering, École Supérieure d’Ingénieurs de Beyrouth, Saint Joseph University, Lebanon

Supervision at Polytechnique


  • Ph.D. (3)

    • Roy, Pankaj Raj. Anomaly recognition in surveillance videos using neural networks.
    • Asaran Darban, Alaleh. Gesture recognition for interactive platforms of manufacturing process.
    • Ceglia, Amedeo. Real-time estimation of muscle forces during an arm pedaling movement by optimization and computer vision for application to rehabilitation.
  • Master (thesis) (14)

    • Baumstimler, Philippe.
    • Lescarbeault, Étienne.
    • Arnold, Cyprien.
    • Zrouki, Doha. Automatic analysis of slit lamp images of the anterior segment of the eye.
    • Faure, Gaspar. Automatic analysis of histological images of the placenta by deep learning.
    • Hubert, Corentin. Hand gesture recognition for interactive manufacturing process platforms.
    • Rodet, Hugo. Infant pose estimation in the pediatric intensive care unit using unsupervised learning.
    • Ebrahimi, Ghazal. Exercice de physiothérapie assisté par ordinateur pour le traitement de la scoliose.
    • Tranchon, Antonin. Multimodal 3D spine segmentation for surgery assistance in adolescent idiopathic scoliosis.
    • Badawy, Youmna. Weakly supervised anomaly detection pipeline for placental histology images.
    • Asmar, Mark. A Vision-Based Automatic Transcription of Guitar Music from RGBD Videos.
    • Cherni, Ghassen. Multimodal deep learning for in-bed human pose estimation.
    • Caron, Rodrigue. Development of a 3D ex vivo platform for the evaluation of micro-damage, strain fields and mechanical properties in trabecular bone.
    • Desclaux, Olivier. Protocol for creating a multimodal 3D pose estimation database of children in the intensive care unit.