Difference between revisions of "PhD"
Jump to navigation
Jump to search
Krahenbuhl (talk | contribs) m (Espaces entre les années) |
|||
| (9 intermediate revisions by 3 users not shown) | |||
| Line 1: | Line 1: | ||
| + | '''Positions for 2024 | ||
| + | * [[:Media:2024_Sujet_these_baudrier.pdf|Surfaces en trois dimensions préservant leurs propriétés géométriques et topologiques sous un processus de discrétisation]] | ||
| + | * [[:media:2024_Naegel_Krähenbühl_Centerline.pdf|Skeletonization of tubular objects by discrete normal accumulation]] | ||
| + | * [[:Media:PhD_Meyer_Faisan.pdf|Uncertainty quantification in automatic delineation for radiation therapy]] | ||
| + | |||
| + | |||
'''Positions for 2023 | '''Positions for 2023 | ||
| − | * [[: | + | * [[:Media:PhDCE2023-ARC-EN-1p.pdf|Preoperative planning and intraoperative guidance for controlled articulated ARC needles]] '''FILLED'''. |
'''Positions for 2021 | '''Positions for 2021 | ||
| − | * [[:Media:PhDCE2020-IRCAD-EN-feb20.pdf|Assistance to trajectory planning and needle guiding for percutaneous surgery]] ''' | + | * [[:Media:PhDCE2020-IRCAD-EN-feb20.pdf|Assistance to trajectory planning and needle guiding for percutaneous surgery]] '''FILLED'''. |
Latest revision as of 14:28, 25 April 2024
Positions for 2024
- Surfaces en trois dimensions préservant leurs propriétés géométriques et topologiques sous un processus de discrétisation
- Skeletonization of tubular objects by discrete normal accumulation
- Uncertainty quantification in automatic delineation for radiation therapy
Positions for 2023
Positions for 2021
Positions for 2020
- Optimization of trajectories of multiple surgical tools using deep learning for percutaneous surgery and SEEG FILLED.
- Real-time replanning of trajectories for percutaneous surgery in deformable environment FILLED.
- Topological and relational guidance for modelling complex anatomical structures
Positions for 2019
- Optimization of trajectories for the simultaneous placement of multiple surgical tools
- Connected operators, hierarchical representations and deep learning.
- Deep learning for segmentation of FIB/SEM images for the diagnosis of chronic lymphocytic leukemia.
Positions for 2017