Gestion et visualisation de données hétérogènes multidimensionnelles : application PLM à la neuroimagerie

Neuroimaging domain is confronted with issues in analyzing and reusing the growing amount of heterogeneous data produced. Data provenance is complex – multi-subjects, multi-methods, multi-temporalities – and the data are only partially stored, restricting multimodal and longitudinal studies. Especially, functional brain connectivity is studied to understand how areas of the brain work together. Raw and derived imaging data must be properly managed according to several dimensions, such as acquisition time, time between two acquisitions or subjects and their characteristics. The objective of the thesis is to allow exploration of complex relationships between heterogeneous data, which is resolved in two parts : (1) how to manage data and provenance, (2) how to visualize structures of multidimensional data. The contribution follow a logical sequence of three propositions which are presented after a research survey in heterogeneous data management and graph visualization.The BMI-LM (Bio-Medical Imaging – Lifecycle Management) data model organizes the management of neuroimaging data according to the phases of a study and takes into account the scalability of research thanks to specific classes associated to generic objects. The application of this model into a PLM (Product Lifecycle Management) system shows that concepts developed twenty years ago for manufacturing industry can be reused to manage neuroimaging data. GMDs (Dynamic Multidimensional Graphs) are introduced to represent complex dynamic relationships of data, as well as JGEX (Json Graph EXchange) format that was created to store and exchange GMDs between software applications. OCL (Overview Constraint Layout) method allows interactive and visual exploration of GMDs. It is based on user’s mental map preservation and alternating of complete and reduced views of data. OCL method is applied to the study of functional brain connectivity at rest of 231 subjects that are represented by a GMD – the areas of the brain are the nodes and connectivity measures the edges – according to age, gender and laterality : GMDs are computed through processing workflow on MRI acquisitions into the PLM system. Results show two main benefits of using OCL method : (1) identification of global trends on one or many dimensions, and (2) highlights of local changes between GMD states.La neuroimagerie est confrontée à des difficultés pour analyser et réutiliser la masse croissante de données hétérogènes qu’elle produit. La provenance des données est complexe – multi-sujets, multi-analyses, multi-temporalités – et ces données ne sont stockées que partiellement, limitant les possibilités d’études multimodales et longitudinales. En particulier, la connectivité fonctionnelle cérébrale est analysée pour comprendre comment les différentes zones du cerveau travaillent ensemble. Il est nécessaire de gérer les données acquises et traitées suivant plusieurs dimensions, telles que le temps d’acquisition, le temps entre les acquisitions ou encore les sujets et leurs caractéristiques. Cette thèse a pour objectif de permettre l’exploration de relations complexes entre données hétérogènes, ce qui se décline selon deux axes : (1) comment gérer les données et leur provenance, (2) comment visualiser les structures de données multidimensionnelles. L’apport de nos travaux s’articule autour de trois propositions qui sont présentées à l’issue d’un état de l’art sur les domaines de la gestion de données hétérogènes et de la visualisation de graphes.Le modèle de données BMI-LM (Bio-Medical Imaging – Lifecycle Management) structure la gestion des données de neuroimagerie en fonction des étapes d’une étude et prend en compte le caractère évolutif de la recherche grâce à l’association de classes spécifiques à des objets génériques. L’implémentation de ce modèle au sein d’un système PLM (Product Lifecycle Management) montre que les concepts développés depuis vingt ans par l’industrie manufacturière peuvent être réutilisés pour la gestion des données en neuroimagerie. Les GMD (Graphes MultidimensionnelsDynamiques) sont introduits pour représenter des relations complexes entre données qui évoluent suivant plusieurs dimensions, et le format JGEX (Json Graph EXchange) a été créé pour permettre le stockage et l’échange de GMD entre applications. La méthode OCL (Overview Constraint Layout) permet l’exploration visuelle et interactive de GMD. Elle repose sur la préservation partielle de la carte mentale de l’utilisateur et l’alternance de vues complètes et réduites des données. La méthode OCL est appliquée à l’étude de la connectivité fonctionnelle cérébrale au repos de 231 sujets représentées sous forme de GMD – les zones du cerveau sont représentées par les noeuds et les mesures de connectivité par les arêtes – en fonction de l’âge, du genre et de la latéralité : les GMD sont obtenus par l’application de chaînes de traitement sur des acquisitions IRM dans le système PLM. Les résultats montrent deux intérêts principaux à l’utilisation de la méthode OCL : (1) l’identification des tendances globales sur une ou plusieurs dimensions et (2) la mise en exergue des changements locaux entre états du GMD

TOWARDS A DATA MODEL FOR PLM APPLICATION IN BIO-MEDICAL IMAGING

International audienceBio-Medical Imaging (BMI) is currently confronted to data issues similar to those of the manufacturing industry twenty years ago. In particular, the need for data sharing and reuse has never been so strong to foster major discoveries in neuroimaging. Some data management systems have been developed to meet the requirements of BMI large-scale research studies. However, many efforts to integrate the data provenance along a research study, from the specifications to the published results, are to be done. Product Lifecycle Management (PLM) systems are designed to comply with manufacturing industry expectations of providing the right information at the right time and in the right context. Consequently PLM systems are proposed to be relevant for the management of BMI data. From a need analysis led with the GIN research group, the BMI-LM data model is designed: it is PLM-oriented, generic (enabling the management of many types of data such as imaging, clinical, psychology or genetics), flexible (enabling users’ customisation) and it covers the whole stages of a BMI study from specifications to publication. The test implementation of the BMI-LM model into a PLM system is detailed. The preliminary feed-back of the GIN researchers is discussed in this paper: the BMI-LM data model and the PLM concepts are relevant to manage BMI data, but PLM systems interfaces are unsuitable for BMI researchers

Management and visualisation oh heterogeneous multidimensional data : PLM application to neuroimaging

La neuroimagerie est confrontée à des difficultés pour analyser et réutiliser la masse croissante de données hétérogènes qu’elle produit. La provenance des données est complexe – multi-sujets, multi-analyses, multi-temporalités – et ces données ne sont stockées que partiellement, limitant les possibilités d’études multimodales et longitudinales. En particulier, la connectivité fonctionnelle cérébrale est analysée pour comprendre comment les différentes zones du cerveau travaillent ensemble. Il est nécessaire de gérer les données acquises et traitées suivant plusieurs dimensions, telles que le temps d’acquisition, le temps entre les acquisitions ou encore les sujets et leurs caractéristiques. Cette thèse a pour objectif de permettre l’exploration de relations complexes entre données hétérogènes, ce qui se décline selon deux axes : (1) comment gérer les données et leur provenance, (2) comment visualiser les structures de données multidimensionnelles. L’apport de nos travaux s’articule autour de trois propositions qui sont présentées à l’issue d’un état de l’art sur les domaines de la gestion de données hétérogènes et de la visualisation de graphes. Le modèle de données BMI-LM (Bio-Medical Imaging – Lifecycle Management) structure la gestion des données de neuroimagerie en fonction des étapes d’une étude et prend en compte le caractère évolutif de la recherche grâce à l’association de classes spécifiques à des objets génériques. L’implémentation de ce modèle au sein d’un système PLM (Product Lifecycle Management) montre que les concepts développés depuis vingt ans par l’industrie manufacturière peuvent être réutilisés pour la gestion des données en neuroimagerie. Les GMD (Graphes Multidimensionnels Dynamiques) sont introduits pour représenter des relations complexes entre données qui évoluent suivant plusieurs dimensions, et le format JGEX (Json Graph EXchange) a été créé pour permettre le stockage et l’échange de GMD entre applications. La méthode OCL (Overview Constraint Layout) permet l’exploration visuelle et interactive de GMD. Elle repose sur la préservation partielle de la carte mentale de l’utilisateur et l’alternance de vues complètes et réduites des données. La méthode OCL est appliquée à l’étude de la connectivité fonctionnelle cérébrale au repos de 231 sujets représentées sous forme de GMD – les zones du cerveau sont représentées par les nœuds et les mesures de connectivité par les arêtes – en fonction de l’âge, du genre et de la latéralité : les GMD sont obtenus par l’application de chaînes de traitement sur des acquisitions IRM dans le système PLM. Les résultats montrent deux intérêts principaux à l’utilisation de la méthode OCL : (1) l’identification des tendances globales sur une ou plusieurs dimensions et (2) la mise en exergue des changements locaux entre états du GMD.Neuroimaging domain is confronted with issues in analyzing and reusing the growing amount of heterogeneous data produced. Data provenance is complex – multi-subjects, multi-methods, multi-temporalities – and the data are only partially stored, restricting multimodal and longitudinal studies. Especially, functional brain connectivity is studied to understand how areas of the brain work together. Raw and derived imaging data must be properly managed according to several dimensions, such as acquisition time, time between two acquisitions or subjects and their characteristics. The objective of the thesis is to allow exploration of complex relationships between heterogeneous data, which is resolved in two parts : (1) how to manage data and provenance, (2) how to visualize structures of multidimensional data. The contribution follow a logical sequence of three propositions which are presented after a research survey in heterogeneous data management and graph visualization. The BMI-LM (Bio-Medical Imaging – Lifecycle Management) data model organizes the management of neuroimaging data according to the phases of a study and takes into account the scalability of research thanks to specific classes associated to generic objects. The application of this model into a PLM (Product Lifecycle Management) system shows that concepts developed twenty years ago for manufacturing industry can be reused to manage neuroimaging data. GMDs (Dynamic Multidimensional Graphs) are introduced to represent complex dynamic relationships of data, as well as JGEX (Json Graph EXchange) format that was created to store and exchange GMDs between software applications. OCL (Overview Constraint Layout) method allows interactive and visual exploration of GMDs. It is based on user’s mental map preservation and alternating of complete and reduced views of data. OCL method is applied to the study of functional brain connectivity at rest of 231 subjects that are represented by a GMD – the areas of the brain are the nodes and connectivity measures the edges – according to age, gender and laterality : GMDs are computed through processing workflow on MRI acquisitions into the PLM system. Results show two main benefits of using OCL method : (1) identification of global trends on one or many dimensions, and (2) highlights of local changes between GMD states

Application of PLM for Bio-Medical Imaging in Neuroscience

Part 10: PLM Virtual and Simulation EnvironmentsInternational audienceBio-medical imaging (BMI) is currently confronted to similar issues than those of manufacturing industries twenty years ago : the growing amount of data, the heterogeneity and complexity of information coming from diverse disciplines, have to be handled by various actors belonging to different organizations. The researchers of the GIN (Neuroimaging Functional Group) laboratory study brain maps of anatomical and functional cognitive activation of hundred-subject cohorts, acquired with Magnetic Resonance Imaging (MRI). Therefore they want to manage the whole process of their research studies, from raw data to analysis results. Even if some data management systems have been developed to meet the requirements of BMI large-scale research studies, there are still many efforts to do in the integration of all the data and processes along a research study, from raw to refined data. So, the use of the Product Lifecycle Management (PLM) concepts to handle the complexity and characteristics of BMI data is proposed. A PLM neuroimaging datamodel that has been designed in collaboration between the GIN laboratory, Roberval laboratory and Cadesis company to meet the needs of the GIN, is described

PLM as a strategy for the management of heterogeneous information in bio-medical imaging field

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Integration and provenance control of proteomics data using SWOMed, a Product Lifecycle Management framework for biomedical research

International audienc

Using Ontologies to Access Complex Data: Applications on Bio-Imaging

International audienceInformation Systems, used to share information, lead to the growth of heterogeneous data and then the dependencies between them. Thus, the links and dependencies among heterogeneous and distributed data are more and more complex during daily activities of users (researchers, engineers, etc.). Our contribution is to propose a methodology to facilitate the exploitation (interrogation and sharing) of complex data in an organization. The system, we propose, tends to mix semantic approach with data management

How to share complex data and knowledge: Application in Bio-Imaging

International audienceInformation Systems, used to share information, lead to the growth of heterogeneous data and then the dependencies between them. Thus, the links and dependencies among heterogeneous and distributed data are more and more complex during daily activities of users (engineers, researchers, etc.). Ontology is currently used to enhance the knowledge sharing and the data integration in many information systems. Our contribution is to propose a methodology to facilitate the exploitation (interrogation and sharing) of data in an organization using Bio-Imaging ontology

Sharing Knowledge in Daily Activity: Application in Bio-Imaging

International audienceOur approach uses the ontology to facilitate the data querying of users in the domain Bio-Imaging where the data resources are heterogeneous and complex. The dependencies among data and the evolution of data resources challenge users (especially for non-technician users) in querying the right data. Ontology can be used to share the users’ understanding about data relationships to all community as well as to trace the database evolution. As consequence, using ontology is a promising solution to facilitate the user’s query making process and to enhance the query’s results

BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

International audienceThe data management needs of the neuroimaging community are currently addressed by several specialized software platforms, which automate repetitive data import, archiving and processing tasks. The BIOMedical Imaging SemanTic data management (BIOMIST) project aims at creating such a framework, yet with a radically different approach: the key insight behind it is the realization that the data management needs of the neuroimaging community—organizing the secure and convenient storage of large amounts of large files, bringing together data from different scientific domains, managing workflows and access policies, ensuring traceability and sharing data across different labs—are actually strikingly similar to those already expressed by the manufacturing industry. The BIOMIST neuroimaging data management framework is built around the same systems as those that were designed in order to meet the requirements of the industry. Product Lifecycle Management (PLM) systems rely on an object-oriented data model and allow the traceability of data and workflows throughout the life of a product, from its design to its manufacturing, maintenance, and end of life, while guaranteeing data consistency and security. The BioMedical Imaging—Lifecycle Management data model was designed to handle the specificities of neuroimaging data in PLM systems, throughout the lifecycle of a scientific study. This data model is both flexible and scalable, thanks to the combination of generic objects and domain-specific classes sourced from publicly available ontologies. The data integrated management and processing method was then designed to handle workflows of processing chains in PLM. Following these principles, workflows are parameterized and launched from the PLM platform onto a computer cluster, and the results automatically return to the PLM where they are archived along with their provenance information. Third, to transform the PLM into a full-fledged neuroimaging framework, we developed a series of external modules: DICOM import, XML form data import web services, flexible graphical querying interface, and SQL export to spreadsheets. Overall, the BIOMIST platform is well suited for the management of neuroimaging cohorts, and it is currently used for the management of the BIL&GIN dataset (300 participants) and the ongoing magnetic resonance imaging-Share cohort acquisition of 2,000 participants