Unit/Project VisAGeS – U1228 (ex U746) is the only one research team jointly awarded by INSERM (National Institute of Health and Medical Research) and INRIA (National Institute of Research in Informatics and Automation). VisAGeS belongs to the IRISA institute (UMR CNRS 6074, University of Rennes I) located at Rennes, France on both medical and physical sciences campus.
Since 70’s, medical imaging is a very rapidly growing research domain, the last three decades have shown a rapid evolution of the dimension and quantity of data physicians have to work with. The next decade will follow this evolution by adding not only new spatio-temporal dimensions to the image data produced and used in a clinical environment but also new scales of analysis (nano or micro biological and molecular images to macros medical images). Another evolution will also consist in adding new effectors during image guided interventional procedures (surgery, interventional radiology…). The classical way of making use of these images, mostly based on human interpretation, becomes less and less feasible. In addition, the societal pressure for a cost effective use of the equipments on the one hand, and a better traceability and quality insurance of the decision making process on the other hand, makes the development of advanced computer assisted medical imaging systems more and more essential.
According to this context, our research team is devoted to the development of new processing algorithms in the context of medical image computing and computer assisted interventions: image fusion (registration and visualization), image segmentation and analysis, management of image related information…). In this very large domain, our work are primarily focused on clinical applications and for the most part on head and brain related diseases.
On the methodological side, our research topics are focussed on
- Image Registration, including:
- Rigid registration
- Deformable registration
- Image segmentation and analysis , including:
- image restauration
- segmentation using deformable shapes
- Statistical analysis in medical imaging:
- Statistical methods for voxel-based analysis
- Creation and exploitation of probabilistic atlases
- Classification and group analysis
- Management of information in medical imaging
- Access and sharing of heterogeneous and distributed information (data, treatments,…)
- Integration in the web and with ontologies (semantic Web)
On the applicative side, our major goals will be focussed on two main domains :
- in Neuroimaging:
- Study, implementation and clinical validation of anatomical and functional atlases
- Study, implementation and clinical validation of new methods of image analysis in multiple sclerosis
- Study, implementation and clinical validation of quantitative tools for cerebral morphometry
- Development of software platforms in clinical environment
- in Image-guided intervention
- Development and clinical validation of methods for 3D freehand ultrasound
- Development and implementation of a medical robotics platform (in partnership with the Lagadic project-team)
International and industrial relations
Inria associated Teams
- NeuroMime associated team
- In the period between 2006 and 2012, this associated team (C. Barillot and L. Collins coordinators) connected Visages with the Montreal Neurological Institute at the Univ. McGill, and more specifically the IPL group of L. Collins in order to combine the respective research in clinical neuroinformatics of the VisAGeS and IPL teams. We aimed at addressing specific aspects of medical image processing for the purpose of neurological disease analysis, especially Multiple Sclerosis and interventional imaging through neurosurgery.
- BARBANT (Boston and Rennes, Brain image Analysis) associated team
- In the period starting in 2013, this associated team is shared between INRIA Visages team and the Computational Radiology Laboratory of the Children’s hospital Boston at Harvard Medical School. It addresses the topic of better understanding the behavior and evolution of neurological pathologies (such as neurodevelopmental delay or multiple sclerosis) at the organ and local level, and the modeling of normal and pathological groups of individuals (cohorts) from image descriptors.