neurolog: sharing neuroimaging data using an ontology

NeuroLOG ANR-06-TLOG-024

Software technologies for integration of process and data in medical imaging

http://neurolog.polytech.unice.fr

Bernard Gibaud1, Gilles Kassel2, Michel Dojat3, Bénédicte Batrancourt4, Franck Michel5, Alban Gaignard5, Johan Montagnat5

1 INSERM / INRIA / CNRS / Univ. Rennes 1, IRISA Unit VISAGES U746, Rennes, France

2 Univ. de Picardie Jules Verne, MIS, EA 4290, Amiens, France 3 INSERM U836 / Univ. J. Fourier, Institut des Neurosciences, Grenoble, France 4 INSERM / CNRS / Univ. Pierre et Marie Curie, CRICM, UMR_S975, Paris, France

5 CNRS / UNS, I3S lab, MODALIS team, Sophia Antipolis, France

AMIA 2011, Washington DC

Supported by

NeuroLOG: sharing neuroimaging data using an ontology-based

federated approach

Software technologies for integration of process, data and knowledge in medical imaging


Collaborative neurosciences

•  Major challenge for this century − Degenerative diseases − Population aging − Pathophysiology understanding − Development / validation of new drugs

•  Need to foster collaborative research − Validation / comparison of methods − Multi-centric disease-targeting studies − Multi-centric cross-disease studies

⇒ Require the sharing of large amounts of resources

2 AMIA 2011, Washington


NeuroLOG ANR-06-TLOG-024 3

Collaborative research in neuroimaging

•  What resources ? –  Image data and associated data (technical, clinical, etc.) –  Image processing tools –  Computing resources

Data

Processing tools Computing power

•  Strategies: 2 possible approaches − Centralized (e.g. ADNI) − Federated (e.g. BIRN,

caBIG, neuGRID, @NeurIST)

AMIA 2011, Washington



Goals of the NeuroLOG project


•  To set up a federated system, allowing the sharing and re-use of: − Neuroimaging data (images and related technical,

demographical and medical metadata) − Processing tools published by cooperating partners − Computer processing resources (local, GRIDs)

•  Three-year project (mid-2007 end-2010) − Supported by ANR − Associating several french partners with complementary

skills ⇒ This presentation focuses on the data sharing

part of the project



Overall system design




•  Federated system –  federating independent legacy systems –  flexibility for data organization

•  Mediation –  use of an a priori-defined ontology –  consistent with a “Local as Views” approach (rather than GaV)

•  Access control –  security layer (X509 certificates + SSL channels) –  security policy

Major design choices

a  Come up with a global federated view that hides data distribution and heterogeneity from the end-user




Ontology design




Ontology: scope

To assemble a common application ontology to provide a uniform and consistent modelling of shared information, e.g. : −  Images (Datasets) −  Image acquisition and image processing (Dataset processings) −  Context of acquisition and exploitation of the images (Studies,

Subjects, Examinations, Centers, etc.) −  Results of other kinds of explorations (Subject data acquisition

instuments, Instrument variables, Assessments, Scores, etc)

Use of this ontology to integrate heterogeneous data Common relational schema




Ontology: general approach

•  Application ontology (called OntoNeuroLOG) −  based on a common modelling framework −  3-level structure

•  one Foundational ontology: i.e. DOLCE •  Several Core ontologies •  Several Domain ontologies

•  Major concerns − Re-use of existing ontologies (when applicable) − Documentation




DOLCE: an ontology of particulars

10

Particular

Endurant Perdurant Quality Abstract

Physical object

Non-Physical object

Mental object

Event Stative

Achievement

Accomplishment

State Process

Social object

Physical quality

Temporal quality

Region

Time interval

(Masolo et al., 2003)




Ontology: 3-level structure

Application ontology (called OntoNeuroLOG) one Foundational ontology (DOLCE) Several Core ontologies Several Domain ontologies




Ontology: 3 representations

1.  OntoSpec representation (Kassel, 2005) Semi-formal notation (rich semantics) Numerous axioms

2.  OWL-Lite Edited with PROTÉGÉ Tailored to perform inferences with CORESE (search

engine) 3.  Federated relational schema

Entities and relations are closely linked to concepts and relations of the ontology



NeuroLOG ANR-06-TLOG-024 AMIA 2011, Washington

Example of OntoSpec representation



OntoNeuroLOG: OWL-Lite representation




Instruments’ descriptions




Subject Study

Dataset

Examination

Experimental Groups of Subjects

Ontology V2.2 Instrument

Variables Scores

Assessment

Federated Relational Schema

Dataset processing

MR protocol




Data integration




Software architecture




Integration mechanism




Data & Metadata management design

write results

read (jdbc interface)

Data Federator

Site 2

Site 1

Metadata manager

get/write files & datasets

Site-specific DB NeuroLOG DB

read

Data Federator

Local storage

resource

Interface with other Services

NeuroLOG Server NeuroLOG Server

Data manager

NeuroLOG Client

Web services

Web services




Metadata mapping

•  Data Federator: relational data mapping and federation tool (Business Object / SAP)

Shanoir

NeuroLOG

GIN-DMS I3S

IRISA GIN I3S

Global federated view

Data Federator /global



Data Federator /local



NeuroLOG NeuroLOG

Global federated schema derived from project ontology (OntoNeuroLOG).




Semantic data




Architecture: semantic module

Semantic repository

Semantic queries engine (CORESE)

METAMorphoses (SQL ↔ RDF)

Images Meta-data (RDF)

Ontology (OWL LITE)

Mapping file (XML) Template file (XML)

Querying

Query interface

Data Federator

Querying

Querying




Software architecture: client software




•  Search datasets related to selected subjects and produced in selected studies

Client – Querying metadata and accessing data




Client - Viewer




Semantic query example (in SPARQL): EDSS scores with ambulation score <= 300m




Results: system deployment




NeuroLOG platform

IFR49 (La Pitié Salpétrière)

IRISA (CHU Rennes)

Paris Rennes

Grenoble

Sophia Antipolis

•  5 sites federated –  4 legacy databases –  12 studies –  > 70 subjects

–  MS –  Brain tumors –  AD

–  > 500 datasets GIN (Michalon Hospital)

I3S site (technical)

Gin-DMS

Neuro-DMS

CAC-DB

INRIA (Centre A Lacassagne)




Discussion




Discussion: ontology

•  Domains that could not be included − Ontology of Regions of Interest (image ROIs) − Ontology of anatomical structures

•  Need of convergence with other ontologies, e.g. NIF − Need to map BFO-based ontologies with our DOLCE-

based ontologies




Discussion: mediator

•  Data federator (SAP): the pros − Standard interface (jdbc) −  Fully dynamic queries − Performance − User interface to defined mappings

•  Data federator (SAP): the cons − No automatic re-configuring in case of error − NOT opensource

•  Alternative solutions − OGSA-DAI / OGSA-DQP (e.g. used in BIRN, @NeurIST) −  Fully semantic integration framework




Conclusion / Perspectives

•  We have presented a system for sharing image data and associated metadata according an ontology-based federated approach -  Most of our objectives (not all !) could be met -  We are currently applying for a continuation of this work in focused

neuroimaging research applications •  We remain convinced that our ontology-based approach

is a promising one –  Intelligent data querying and mediation –  Complementary to regular relational querying

•  However, it raises the issue of the convergence with other ontologies developed elsewhere, e.g. –  NIF* / BIRN** in the USA –  @NeurIST in Europe

* Neuroscience Information Framework ** Biomedical Informatics Research Network




Acknowledgements

•  ANR, for its financial support •  All the people having contributed to NeuroLOG, espec. −  Farooq Ahmad −  Christian Barillot −  Pascal Girard −  David Godard −  Diane Lingrand −  Grégoire Malandain −  Mélanie Pélégrini-Issac −  Xavier Pennec −  Javier Rojas Balderrama −  Bacem Wali

•  And all our clinical partners, especially −  Gilles Edan, Jean-Christophe Ferré and Jean François Lebas


neurolog: sharing neuroimaging data using an ontology

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