Computer-Assisted Medical Interventions (CAMI) Assistant


The chair is concerned with the use of Artificial Intelligence in the context of developing computer assistance to medical and surgical interventions. This includes AI-based image processing and calibration, AI-based simulation, extracting knowledge from intervention tracks to be able to model intervention quality. Context-aware assistance and more autonomous assisting devices (including robots) are also considered in the chair.
The chair is conducted in close collaboration with clinical teams and industrial partners.


The work already undertaken is about the use of deep learning for automatic calibration of interventional CBCT imaging systems. Learning is also being studied for image processing and fusion in order to allow real-time guidance of diagnostic or treatment procedures such as prostate biopsies and neurosurgery. In these two cases, ultrasound imaging has a predominant role during the operation and raises big challenges in terms of image processing. In addition, we are developing new robotic approaches based on continuous robots with a medium-term objective of endoluminal use.


Structuring activity on AI at the national level with CAMI partners. Working group about AI for Health in TIMC and CIC/CHU Grenoble Alpes.


  • Dupuy, T., Beitone, C., Troccaz, J., & Voros, S. (2021). 2D/3D Deep registration for real-time prostate biopsy navigation. Accepted In SPIE Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling. February 2021.
  • Younes, H., Troccaz, J., & Voros, S. (2021). Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy. Medical Physics.
  • Derathé A, Reche F, Jannin P, Moreau-Gaudry A, Gibaud B, Voros S. Explaining a model predicting quality of surgical practice: a first presentation to and review by clinical experts. Int J Comput Assist Radiol Surg. 2021 Jun 18. doi: 10.1007/s11548-021-02422-0. Epub ahead of print. PMID: 34143373.
  • Carton, F. X., Chabanas, M., Munkvold, B. K., Reinertsen, I., & Noble, J. H. (2020). Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net. In SPIE Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 11315, p. 113150S), Houston, March 2020.
  • Lapouge, G., Younes, H., Poignet, P., Voros, S., & Troccaz, J. (2019). Needle Segmentation in 3D Ultrasound Volumes Based on Machine Learning for Needle Steering. Hamlyn Symposium on Medical Robotics, London, June 2019.
Published on  January 23, 2024
Updated on January 23, 2024