The BIOMIST (BIO Medical research Imaging SemanTic data management) project is an ANR (Agence Nationale de la Recherche) founded project (n° ANR-13-CORD-0007) for thematic axis n°2 of the Contint 2013 Call for Proposal: from content to knowledge and big data. It began November 1st 2013 and will end April 30th 2017.
The objective is to provide researchers in the field of biomedical imaging (BMI) with an efficient information system in order to help them make optimal use of their data during their research activities on large group of subjects, and to enable the reuse of available data for clinic and fundamental research in a context and for a purpose other than the one for which they have been acquired.
The partners for this project are Cadesis (a R&D SME specialized in the integration of Information Systems for the industry), Groupe d'Imagerie Neurofonctionnelle (GIN - UMR 5296) a core member of the LabEx TRAIL "Investissements d'avenir", Laboratoire Roberval (UMR 7337 - Université Technologique de Compiègne) and Institut Charles Delaunay (ICD - UMR 6279 - Université Technologique de Troyes).
With the GIN research team we will focus on the neuro-functional imaging field as a testbed for our project. Besides the images (2D, 3D, 4D), we will manage every type of data required such as demographic data, behavioral test results and as a new topic in this field: genetic information. A special focus will be on both intra and inter subject analysis definition and results with respect to the material used for scientific publications. Our project will aim to manage not only documents but, more significantly, the specific concepts used in neuro-functional analysis such as cognitive stimulation paradigms, processing tasks, behavioral test definitions… and all the semantic relationships that may exist between them.
We aim to provide methodologies and tools to manage the growing amount, complexity and provenance of BMI data and to consider, not only the data, but also their usage, their different representation and their interpretation in the context of neuro-functional research. We propose to use a proved foundation used in traditional engineering to answer the basic requirements for BMI data management: Product Lifecycle Management (PLM) solutions. Since our application domain is a research environment, it demands more flexibility than what is currently available from PLM solutions; we then propose to use Knowledge Management (KM) techniques to enhance reuse and traceability in the usage of BMI data in a complex and changeable environment. Furthermore we will provide and integrate visualization and analysis tools that enable users to make hypothesis, intuitively discover and compare patterns, and isolate structure singularities in graph representation of data that could be used for semantic relationships or brain connectivity graphs (a specific neuro-functional representation).
The results of the project will be a working prototype deployed, validated and used by the GIN team. The technology transfers between academic partners and Cadesis during the project are expected to lead to exploitable results such as the availability of a BioMedical Imaging module for PLM solutions, the enhancement of Knowledge-based reuse methodologies and the development of graph visualization tools and algorithms that could be applied to a wide range of domains.