GALEN-POST Research team
Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
- Leader : Jean-christophe Pesquet
- Type : team
- Research center(s) : Saclay
- Field : Digital Health, Biology and Earth
- Theme : Computational Neuroscience and Medicine
- Inria teams are typically groups of researchers working on the definition of a common project, and objectives, with the goal to arrive at the creation of a project-team. Such project-teams may include other partners (universities or research institutions)
Recent developments on the hardware side have let to a new generation of scanners as well as image modalities where the in vivo visualization of anatomical structures of biological systems is possible in a non invasive fashion. The exploitation of such an information space is a great challenge of our days and consists of understanding the anatomical structure of biological systems and in particular the effect of pathologies on their complex mechanisms of operation. Such in depth modeling and understanding of complex biological systems is an interdisciplinary effort which involves researchers with different scientific origins including physiology, biology, neurobiology, mathematics and engineering. Within such a context, modeling complex anatomical structures often consists in three steps;
- recovering a set of measures, anatomical and pathological indexes through the processing, understanding and exploitation of medical image modalities,
- proposing a (parametric) mathematical model that is consistent with the anatomy and is capable of describing the operation of the organ/structure under consideration, and
- estimating the parameters of the model such that it can reproduce the behavior observed/supported through the use of anatomical and pathological indexes.
Despite enormous progress made on computer-aided diagnosis, exploiting organ aging information has gained little attention. The medical imaging community has primarily focused on image-aided mid-to-short term diagnosis using certain pre-clinical indicators/risk factors. Long-term modeling and understanding the effects of aging is critical to a number of organs and diseases that do not present pre-disease indicators like brain neurological diseases, muscular diseases, certain forms of cancer, etc. Obviously such a task requires the ability to extract information from images, however one should go further and provide mathematical models explaining the temporal evolution of these measurements. The main research direction that we are planning to develop in the next decade consists of building organ evolution models due aging from images. To this end, we imagine a set of subjects imaged with a certain frequency using non-invasive means, content extraction from theses images like the volume, form, tissue properties, etc. and mathematical modeling of these parameters using non-linear time series models that couple anatomical and pathological indexes. Such an approach will provide:
- a better understanding of the aging process,
- means of recovering risk factors for certain diseases at very early stage,
- means of diagnosis for long-term pathologies without pre-clinical symptoms.
Parallel to that an effort to understand the importance of emerging modalities in the domain will be carried on.
International and industrial relations
- Clinical : Centre Hospitalier Universitaire Henri Mondor, Creteil - Assistance Publique-Hôpitaux de Paris, Pitie SalPetriere, Paris - Assistance Publique-Hôpitaux de Paris, Hopital Beaujon, Paris - Institut de Myologie : AFM / Assistance Publique-Hôpitaux de Paris / Inserm / l'Université Paris VI / CEA - Commissariat à l'Énergie Atomique
- Industrial : BiospaceLab (http://www.biospacelab.com), Intrasense (http://www.intrasense.fr/), Siemens (http://www.siemens.com )
- Academic : Supelec, Ecole de Ponts, Ecole Polytechnique Fédérale de Lausanne, Technical University of Munich, University of Crete, Yale University
Research teams of the same theme :
- ARAMIS - Algorithms, models and methods for images and signals of the human brain
- ATHENA - Computational Imaging of the Central Nervous System
- BIOVISION - Biologically plausible Integrative mOdels of the Visual system : towards synergIstic Solutions for visually-Impaired people and artificial visiON
- CAMIN - Control of Artificial Movement & Intuitive Neuroprosthesis
- EMPENN - EMPENN
- EPIONE - E-Patient: Images, Data & MOdels for e-MediciNE
- MATHNEURO - Mathematics for Neuroscience
- MIMESIS - Computational Anatomy and Simulation for Medicine
- MNEMOSYNE - Mnemonic Synergy
- NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
- PARIETAL - Modelling brain structure, function and variability based on high-field MRI data.