Section: Partnerships and Cooperations

European Initiatives

Collaborations in European Programs, except FP7

• Program: European Research Council

• Project acronym: DIOCLES

• Project title: Discrete bIOimaging perCeption for Longitudinal Organ modEling and computEr-aided diagnosiS

• Duration: mois année début - mois année fin 9/2011-8/2016

• Coordinator: N. Paragios

• Abstract: Recent hardware developments from the medical device manufacturers have made possible non-invasive/in-vivo acquisition of anatomical and physiological measurements. One can cite numerous emerging modalities (e.g. PET, fMRI, DTI). The nature (3D/multi-phase/vectorial) and the volume of this data make impossible in practice their interpretation from humans. On the other hand, these modalities can be used for early screening, therapeutic strategies evaluation as well as evaluating bio-markers for drugs development. Despite enormous progress made on the field of biomedical image analysis still a huge gap exists between clinical research and clinical use. The aim of this proposal is three-fold. First we would like to introduce a novel biomedical image perception framework for clinical use towards disease screening and drug evaluation. Such a framework is expected to be modular (can be used in various clinical settings), computationally efficient (would not require specialized hardware), and can provide a quantitative and qualitative anatomo-pathological indices. Second, leverage progress made on the field of machine learning along with novel, efficient, compact representation of measurements toward computer aided diagnosis. Last, using these emerging multi-dimensional signals, we would like to perform longitudinal modeling and understanding the effects of aging to a number of organs and diseases that do not present pre-disease indicators such as brain neurological diseases, muscular diseases, certain forms of cancer, etc. Such a challenging and pioneering effort lies on the interface of medicine (clinical context), biomedical imaging (choice of signals/modalities), machine learning (manifold representations of heterogeneous multivariate variables), discrete optimization (computationally efficient infer- ence of higher-order models), and bio-medical image inference (measurements extraction and multi-modal data fusion of heterogeneous information sources). The expected results of such an approach are societal and scientific. The societal impact can be tremendous since we aim to provide novel means of using emerging biomedical signals to help physicians diagnose, select, customize and follow up therapeutic strategies for life-threatening diseases. Concerning scientific impact, this framework could influence and introduce novel means of re-thinking old, unsolved problems in a number of areas such us bioinformatics, geometric modeling, robotics, computer vision, multimedia, etc.

Major European Organizations with which you have followed Collaborations

• Partner 1: Technical University of Munich, Chair for Computer Aided Medical Procedures & Augmented Reality - Computer Science Department (Germany)

• Mono and Multi-modal image fusion using discrete optimization and efficient linear programming.

• Partner 2: University of Crete, Computer Vision Group - Computer Science Department, (Greece)

• Linear Programming, relaxations and efficient optimization of pair-wise and higher order Markov Random Fields.

• Partner 3: Eidgenössische Technische Hochschule (ETH) - Zürich, Seminar für angewandte Mathematik - Mathematics Department, (Switzerland)

• Sparse Representations and Optimal Linear Registration of Volumetric Medical Image Data.