VisAGeS U746 at Short*: 1 image = 1 2D MRI slice 08/05/08 4 Neuroinformatics in the context of CNS...

1st CRM-INRIA-MITACS Meeting 08/05/08

Christian BARILLOT 1

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NeuroinformaticsNeuroinformatics in the context of CNS in the context of CNS diseases diseases

Christian BARILLOTUnit/Project VisAGeSVisAGeS -- U746, U746, INRIA/INSERM

IRISA, CNRS UMR6074, Univ. Rennes 1Rennes, France

http://www.irisa.fr/visages

1st First CRM-INRIA-MITACS Meeting : Centre de recherches mathématiques (CRM), Univ. Montréal

08/05/0808/05/08 22

VisAGeSVisAGeS U746 at ShortU746 at Short

4 affiliations4 affiliationsINSERMINSERM (National Institute of Health and Medical Research) (National Institute of Health and Medical Research)

INRIAINRIA (National Research Institute of Informatics and Automation)(National Research Institute of Informatics and Automation)

Through IRISAThrough IRISA::University of Rennes IUniversity of Rennes ICNRSCNRS (National Center for Scientific Research)(National Center for Scientific Research)

The only team to be jointly affiliated to INSERM and INRIAThe only team to be jointly affiliated to INSERM and INRIAOffices in 2 locations :Offices in 2 locations :

Univ. Hospital and IRISA/INRIA (Univ. Hospital and IRISA/INRIA (15mn by car15mn by car))Transparent Transparent ““virtualvirtual”” office (office (network, admin, agendanetwork, admin, agenda))People have office at both locationsPeople have office at both locations

Joint INRIA International Team with U. McGill, Montreal (Pr. L. Joint INRIA International Team with U. McGill, Montreal (Pr. L. Collins)Collins)



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General context and challenges in General context and challenges in clinical neuroimagingclinical neuroimaging

Context :Context :

Expansion of the quantity of data produced Expansion of the quantity of data produced and processed in medical imaging (and processed in medical imaging («« from the from the volume to the massvolume to the mass »»))

Explosion of the IST and the electronic Explosion of the IST and the electronic communication resourcescommunication resources

Challenges :Challenges :

To guide the clinician (e.g. a neurologist) To guide the clinician (e.g. a neurologist) within the mass of information to integrate within the mass of information to integrate into the medical decision processinto the medical decision process

To guide the surgeon for the exploitation of To guide the surgeon for the exploitation of the different sensors and effectors (e.g. the different sensors and effectors (e.g. robots) to use in the interventional theaterrobots) to use in the interventional theater

70's 80's 90's 2000's 2010's

Molecular & Molecular & BiologyBiology imagesimages

fMRIfMRI3D PET 3D PET SPECTSPECT

3D CT3D CT

DTDT--MRIMRI

3D MRI3D MRI2D CT2D CT0,5 MB

1 GB

►► MS lesionsMS lesions12000 images*/patient/year12000 images*/patient/year

►► Epilepsy surgeryEpilepsy surgery7000 images*/intervention7000 images*/intervention

*: 1 image = 1 2D MRI slice

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NeuroinformaticsNeuroinformatics in the context in the context of CNS diseasesof CNS diseases

Challenges for tomorrow researchChallenges for tomorrow research on brain diseaseson brain diseasesConception of the surgical room of the futureConception of the surgical room of the future

Better understand the behavior of normal and pathological brain Better understand the behavior of normal and pathological brain systems, at different scalessystems, at different scales

Set up new computer and network infrastructures for research in Set up new computer and network infrastructures for research in clinical neurosciencesclinical neurosciences



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VisAGeSVisAGeS contribution in contribution in NeuroinformaticsNeuroinformatics::

Translational ResearchTranslational ResearchPathologies of the Central Nervous SystemPathologies of the Central Nervous System

Neurological PathologiesNeurological Pathologies►► Multiple Sclerosis, Multiple Sclerosis, epilepsy, dementia, epilepsy, dementia, neuroneuro--degenerative degenerative

disease , OCD, psychiatry disease , OCD, psychiatry ……))

Image Guided NeurosurgeryImage Guided Neurosurgery►► IntraIntra--operative imagery in neurosurgeryoperative imagery in neurosurgery

Basic Research ObjectivesBasic Research ObjectivesInformation fusion in healthInformation fusion in health

►► Linear and Non Linear registrationLinear and Non Linear registration

Medical image Computing Medical image Computing ►► Image restoration, Integration of new a priori models (atlas, Image restoration, Integration of new a priori models (atlas,

statistics, computational anatomy)statistics, computational anatomy)

Management of information in medical imagingManagement of information in medical imaging►► Integration of heterogeneous and distributed resourcesIntegration of heterogeneous and distributed resources

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NonNon--RigidRigid RegistrationRegistration

Sub

ject

s D

ata

Bas

eS

ubje

cts

Dat

a B

ase

Global RegistrationGlobal Registration

Local RegistrationLocal Registration

Segmented Segmented SulciSulci

Registered Registered SulciSulci

Statistical Statistical Model of Model of SulcusSulcus XX

Probability Probability of of SulcusSulcus XX

Probability of Probability of Activation YActivation Y

Dense Matching Dense Matching to Reference to Reference

BrainBrain

Probability of Probability of SulcusSulcus XX


Hybrid RegistrationHybrid Registration

Hybrid Matching Hybrid Matching to Reference to Reference SulcusSulcus and and

BrainBrain

Probability of Probability of SulcusSulcus XX


+



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NonNon--rigidrigid registrationregistrationRomeoRomeo©© (photometric registration) (photometric registration) [TMI 01][TMI 01]JulietJuliet©© (hybrid registration) (hybrid registration) [TMI 03][TMI 03]

Validation : International project Validation : International project [TMI 03] [TMI 02][TMI 03] [TMI 02]

Shaped based probabilistic atlases Shaped based probabilistic atlases [[NeuroimageNeuroimage 03] [Media 04]03] [Media 04]

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NonNon--rigidrigid registrationregistrationRomeo (photometric registration) Romeo (photometric registration) [TMI 01][TMI 01]Juliet (hybrid registration) Juliet (hybrid registration) [TMI 03][TMI 03]





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NonNon--rigidrigid registrationregistrationRomeo (photometric registration) Romeo (photometric registration) [TMI 01][TMI 01]

Juliet (hybrid registration) Juliet (hybrid registration) [TMI 03][TMI 03]Validation : International project Validation : International project [TMI 03] [TMI 02][TMI 03] [TMI 02]

Shaped based probabilistic atlases Shaped based probabilistic atlases [[NeuroimageNeuroimage 03] 03]

U. McGillU. McGill EpidaureEpidaureAffineAffine

TalairachTalairach VisagesVisages TargetTarget

U. IowaU. Iowa

SPMSPM

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NonNon--rigidrigid registrationregistrationRomeo (photometric registration) Romeo (photometric registration) [TMI 01][TMI 01]Juliet (hybrid registration) Juliet (hybrid registration) [TMI 03][TMI 03]



V1

V2d

V2vV3v

V3vV3A

V4Collaboration INSERM GrenobleCollaboration INSERM Grenoble



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NonNon--rigidrigid registrationregistration for for modeling asymmetriesmodeling asymmetries

Objective :Objective : quantify the asymmetries bilateral structures (face, brain, quantify the asymmetries bilateral structures (face, brain, caudate nuclei, ventricles...) from surfaces.caudate nuclei, ventricles...) from surfaces.Method1.1. Fine and robust estimation of the symmetry plane.Fine and robust estimation of the symmetry plane.2.2. Non linear registration of bilateral surfaces to map the asymmetNon linear registration of bilateral surfaces to map the asymmetriesries3.3. Projection of the asymmetry maps on to an atlas for statistical Projection of the asymmetry maps on to an atlas for statistical analysisanalysis

Results (ref = expansion ; blue = atrophy):

[Combès et Prima (CVPR’2008, ISBI’2008]

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NonNon--local means for image local means for image restoration and noise reductionrestoration and noise reduction

IdeaIdea : : Start from the theoretical formulation [BuadesStart from the theoretical formulation [Buades--05] in order to 05] in order to restore restore image intensities obtained by a nonimage intensities obtained by a non--local weighted mean of all local weighted mean of all voxelsvoxels

ChallengeChallenge : : Improve the performance (in time and quality) et adapt the Improve the performance (in time and quality) et adapt the method to different medical image modalities and image dimensionmethod to different medical image modalities and image dimensionss

SolutionSolution: : Generalization to Generalization to nnDD images, Decomposition images, Decomposition of images in subof images in sub--volumes volumes VVii and adaptation of the and adaptation of the weighting metric to local statistics of the observed dataweighting metric to local statistics of the observed data

AdvantagesAdvantages : : Fast Implementation Fast Implementation ((6 h 6 h --> 7 min for a 256> 7 min for a 2563 3 volumevolume))

Outperform current methods Outperform current methods (PSNR ~ +3DB)(PSNR ~ +3DB)

Adaptable to a wide range of image modalities Adaptable to a wide range of image modalities (MRI, DTI, Ultrasounds, Optical(MRI, DTI, Ultrasounds, Optical……))Original Processed

Restoration of MR Images in Multiple

Sclerosis

Segmented

Restoration of Diffusion Tensors MR

images(Rician model)

[N. Wiest-Daeslé et al., Miccai’07]Original Processed

1 direction

Original Processed

200 directionsRestoration of 3D ultrasound images

(Rayleigh model)

[P. Coupe et al, Patent]

Rés

ults

[P. Coupe et al., MICCAI 2006, TMI-07]



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ModelModel--Guided Segmentation and Labeling:Guided Segmentation and Labeling:Integration of fuzzy control and level sets*Integration of fuzzy control and level sets*

Objective :Objective : Segmentation of brain Segmentation of brain structures close, with similar intensities structures close, with similar intensities and hardly defined contoursand hardly defined contoursMethod :Method :

Statistical analysis of shape and Statistical analysis of shape and localization of structureslocalization of structuresConcurrent evolution of several Concurrent evolution of several level setslevel sets

Contribution :Contribution :Integration of fuzzy control to Integration of fuzzy control to constrain the competitive evolution constrain the competitive evolution of level setsof level setsUtilization of a statistical shape Utilization of a statistical shape models to define the fuzzy control models to define the fuzzy control variablesvariables

*: C. Ciofolo, C. Barillot, IPMI 2005, ECCV 2006

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Objective :Objective : use use multisequencemultisequence MRI and scale space to endMRI and scale space to end--up with fast up with fast and semiand semi--automatic segmentationautomatic segmentation

Segmentation using spectral Segmentation using spectral gradient and graph cutgradient and graph cut

Method1. Create a color image from MRI sequences.2. Compute the spectral gradient3. Transform the image into a graph4. Compute the minimal cut from the spectral gradient5. Back transform the graph into image

Results :Results :Brain tumor and edema segmentation Brain tumor and edema segmentation Segmentation of Multiple Sclerosis LesionSegmentation of Multiple Sclerosis Lesion

T1 MRI T2 MRI Results



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Preparation of the surgeryPreparation of the surgeryTarget SelectionTarget Selection

Plan the direction to the targetPlan the direction to the target

Guide the surgeonGuide the surgeon

NeuroInformatsNeuroInformats in Neurosurgery:in Neurosurgery:Image Guided Neurosurgical Image Guided Neurosurgical

proceduresprocedures

ChallengeChallengeReach the target, remove it while preserving the eloquent Reach the target, remove it while preserving the eloquent tissues, to tissues, to ::

Reduce morbidity, handicap and mortalityReduce morbidity, handicap and mortality

Define new surgical protocolsDefine new surgical protocols

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Preoperative Planning :Preoperative Planning :Anatomical and Anatomical and Functional MappingFunctional Mapping



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ImageImage--Guided Neurosurgery:Guided Neurosurgery:Interventional procedure (Interventional procedure (NeuronavigationNeuronavigation))

3D referential system

Patient

3D localizer

Surgical Microscope3D workstation

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Brain ShiftBrain Shift

Adding observations during Adding observations during surgery: Video reconstructionsurgery: Video reconstruction

Video Based 3D reconstructionVideo Based 3D reconstruction



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Before After

Rigid registration of intraRigid registration of intra--operative 3D operative 3D freefree--hand ultrasound with MRIhand ultrasound with MRI

*: P. Coupe et al.,IEEE-ISBI 2007

Objective: Construct probability maps of hyperechogenic structures from MRI and Ultrasound images for registration.Principal: Find a function frelating the MRI intensity of a voxel X (u(X)) with its probability to be included in the set of hyperechogenic structures :

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NonNon--rigid registration of intrarigid registration of intra--operative 3D freeoperative 3D free--hand ultrasound hand ultrasound

with MRIwith MRI

Objective: Compensate for the intra-operative deformations after opening of brain envelopes

Method: Non rigid transformation using a multi-[P. Coupe et al., Patent]

ResultsValidation on a synthetic deformationExperimentation on 5 patients

mean estimated deformation 2.71 +/mean estimated deformation 2.71 +/-- 1.03 mm1.03 mm

mean estimated deformationmean estimated deformation 1.81 +/1.81 +/-- 1.02 mm1.02 mm



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ImageImage--guided neurosurgical procedures:guided neurosurgical procedures:Current and New IssuesCurrent and New Issues

Integration of new models and observationsIntegration of new models and observations

Take into account intraoperative brain deformation Take into account intraoperative brain deformation (gravity, drugs, CSF leaks de LCS, (gravity, drugs, CSF leaks de LCS, exexéérrèèsese ……))

Take into account additional preoperative data (DTI, molecular)Take into account additional preoperative data (DTI, molecular)

New information sensors during surgery (New information sensors during surgery (video, 3D ultrasound, video, 3D ultrasound, iMRIiMRI, in, in--vivo microscopic biological vivo microscopic biological imaging, molecular dataimaging, molecular data))

““Real timeReal time”” fusion of multimodal intraoperative images to assist the decisifusion of multimodal intraoperative images to assist the decision processon process

Intraoperative Intraoperative ImagingImaging Fu

sion

of

Fusi

on o

f ““ O

bser

vatio

nsO

bser

vatio

ns””

Preoperative Preoperative ImagingImaging

Processing of surgical Processing of surgical ““observationsobservations””

2.5D Image

RevisionRevision

ControlControl

Numerical ModelsNumerical Models(a posteriori knowledge)

Modelling of Modelling of surgical proceduressurgical procedures

(a priori knowledge)

Processing of Processing of ““Knowledge DataKnowledge Data””

3D Ultrasound

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Integration of new pre and intraIntegration of new pre and intra--operative data and surgical modelsoperative data and surgical models

New sensors :New sensors :

DTI imagingDTI imaging

Molecular Imaging and dataMolecular Imaging and data

VideoVideo

3D Ultrasound3D Ultrasound

Interventional MRIInterventional MRI

In vivo Biological Imaging (In vivo Biological Imaging (confocalconfocal microscopy)microscopy)

SPL-

Har

vard

©MaunaKeaTech©



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NeuroinformaticsNeuroinformatics in Neurological in Neurological PathologiesPathologies

MeansMeansImaging of the pathologies : from the organ to the cell and Imaging of the pathologies : from the organ to the cell and the moleculethe molecule

ChallengeChallengeEarly DiagnosisEarly Diagnosis

Therapy more specificTherapy more specific

Prevention of disease progressionPrevention of disease progression

Better understanding of the natural history of the pathologyBetter understanding of the natural history of the pathology

Evaluation of new therapeutic protocols Evaluation of new therapeutic protocols

Better understand the normal and pathological brain to better caBetter understand the normal and pathological brain to better carere

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Multiple SclerosisMultiple Sclerosis -- a twoa two--stage stage diseasedisease

Natural Evolution of Natural Evolution of Multiple Sclerosis diseaseMultiple Sclerosis disease

0

1

2

3

4

5

6

7

0 5 10 15 20 25 30years

EDSS

Source: G. Edan, E. Leray, Etude sur 2054 patients SEP, CHU Rennes

Clin

ical

Sco

re

Years



08/05/0808/05/08 2525…

t 1

t 2

t n

Parametric estimation of

“normal” tissues

Identification of “irregular” data

Neurological Expertise

… t n

t 1

t 2

Objective: Objective: Find markers of evolution (Find markers of evolution (lesionslesions))

Automatic segmentation of MS Automatic segmentation of MS LesionsLesions

T1-w 3D T2-w 3D FLAIR 3D

Classification and fusion of MRI examsClassification and fusion of MRI exams

BeforeBefore denoising/debiaisdenoising/debiaisSTREM v1: Detection of abnormal tissuesSTREM v1: Detection of abnormal tissuesSTREM v2: Initialization + rules STREM v2: Initialization + rules OrangeOrange: Good Detection: Good Detection RougeRouge: Over detection: Over detection VertVert: under: under--DetectionDetection BleuBleu: non detection: non detection

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Diffusion Tensor MRI markers in Diffusion Tensor MRI markers in Multiple SclerosisMultiple Sclerosis

ResultsFA and MD in MS Lesions ≠ FA and MD in MS controlateral

FA and MD in MS controlateral ≠FA and MD in controls

[N. Wiest-Daesslé et al., Eur. Neur. Soc. 2008]* Support of ARSEP

Objective: Analyze the level of demyelinization of the WM around MS lesions

MethodDefinition of the mid-sagittalplane

Estimation of diffusion parameters in WM lesions

Estimation of diffusion parameters in controlateral regions

Analysis of variance with multiple comparison test

Lesions Controlateral Controls



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Neuroimaging in Multiple Sclerosis:Neuroimaging in Multiple Sclerosis:EvolutionEvolution

Current NeuroimagingCurrent NeuroimagingIn general: nonIn general: non--specific focal inflammationspecific focal inflammation

NonNon--specific MRspecific MR--parametersparameters

EvolutionEvolutionDevelop new specific Develop new specific NeuroimagingNeuroimaging biomarkersbiomarkers

New New ““staticstatic”” imaging of white matter (DTI, MT, imaging of white matter (DTI, MT, ……))

Cell labeling imaging techniques (macrophages, activated microglCell labeling imaging techniques (macrophages, activated microglia)ia)MRI (e.g. USPIOMRI (e.g. USPIO))

PET (e.g. PK 11195)PET (e.g. PK 11195)

Develop new Neuroimaging protocols to study:Develop new Neuroimaging protocols to study:The four distinct MS lesion patternsThe four distinct MS lesion patterns

Focal versus diffuse MS pathologyFocal versus diffuse MS pathology

Cortical MS pathologyCortical MS pathology

Collaboration with MNI @ U. McGill Collaboration with MNI @ U. McGill (L. Collins, D. Arnold)(L. Collins, D. Arnold), , U. of Texas @ HoustonU. of Texas @ Houston (J (J WolinskyWolinsky, P. , P. NarayanaNarayana)),, ……

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Exemplesof MSA

Medical Image Computing in Neurological Diseases:Voxel based morphometry in Parkinsonian disorders*

Objective:Objective:Differentiate between Differentiate between Parkinson’s Disease (PD) and MSA (Multiple Systems Atrophy) and PSP (Progressive SupranuclearPalsy) symptoms (current : 66% TP)Early diagnosis from cross-sectional MRI at a single time point at inclusion

Result:Only 20 patient from each group90% differential diagnosis PD vs MSA/PSPError rate cut by 50%

Normal Mild Severe

*: S. Duchesne et al., SPIE 2007



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Surface based Surface based morphometrymorphometry in in ParkinsonianParkinsonian disordersdisorders

[D. Tosun et al., Miccai’07]

ObjectiveObjective::Differentiation between Differentiation between ParkinsonParkinson’’s s Disorders Disorders

Early diagnosis from crossEarly diagnosis from cross--sectional MRI at a single time point sectional MRI at a single time point at inclusionat inclusion

MethodExtraction of GM/WM interfaceExtraction of GM/WM interface

Computation of cortical indexesComputation of cortical indexes

Results:Only 20 patient from each groupOnly 20 patient from each group

Mapping of statistical difference Mapping of statistical difference between groupsbetween groups Cortical Cortical

ThicknessThickness

BrainBrainCurvatureCurvature

SulcalSulcal DepthDepth

geodesicgeodesic sulcalsulcal depthdepth

cortical gray cortical gray mattermatter thicknessthickness

Cortical Feature Significant Mean Difference Maps

Cortical Feature Population Average Maps

RedRed//YellowYellow: IPD > MSA, IPD > PSP, MSA> PSP: IPD > MSA, IPD > PSP, MSA> PSPBlueBlue//CyanCyan: : MSA>IPD, PSP>IPD, PSP>MSAMSA>IPD, PSP>IPD, PSP>MSA

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Management of information Management of information resources in clinical neuroimagingresources in clinical neuroimaging

Objectives (Objectives (from the French from the French NeurobaseNeurobase projectproject) :) :Follow the growth of the communication and exchange infrastructuFollow the growth of the communication and exchange infrastructures (e.g. res (e.g. Internet)Internet)Follow the emergence of "virtual" organizations of users (e.g. cFollow the emergence of "virtual" organizations of users (e.g. clinical groups of linical groups of research)research)

Applications of information and grids technologies in health:Applications of information and grids technologies in health:Creation of "virtual" cohortsCreation of "virtual" cohortsResearch on the singular diseases (search for Research on the singular diseases (search for «« unlikely facts unlikely facts »») ) from image from image descriptorsdescriptorsDrug certification from inDrug certification from in--vivo imagingvivo imaging

Research IssuesResearch IssuesCombine Grid Computing and Semantics Grids technologies in the fCombine Grid Computing and Semantics Grids technologies in the field of medical ield of medical imagingimagingEvolutiveEvolutive and adaptive workflows in Medical Imaging (user interactions, and adaptive workflows in Medical Imaging (user interactions, heterogeneity, heterogeneity, ……))Integrate the semantic web technologies into clinical researchIntegrate the semantic web technologies into clinical research

Contacts with BIRN, NAMIC, Contacts with BIRN, NAMIC, CaBIGCaBIG projectsprojects



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Integration of heterogeneous and distributed Integration of heterogeneous and distributed resources in neuroimaging (resources in neuroimaging (NeuroBaseNeuroBase ProjectProject))

Objectives:Objectives: Federate heterogeneous and distributed Federate heterogeneous and distributed resources (resources (images, processing toolsimages, processing tools) in neuroimaging) in neuroimaging

ResultsResultsGeneric architecture to Generic architecture to integrate heterogeneous and integrate heterogeneous and distributed resources distributed resources

Universal semantic model for Universal semantic model for the sharing of resourcesthe sharing of resources

Interfacing around a Interfacing around a mediation middlewaremediation middleware

Development and on site Development and on site exploitation of a test bed exploitation of a test bed systemsystem

IRIS

A_N

ET

IRIS

A_N

ET

INTERNETINTERNET

WebAppWebApp

IRISA (IRISA (w3extw3ext))

TomCatTomCat

ApacheApache

5517; 3060

8080

Le SelectLe Select

GrenobleGrenoble

boot server

5517; 3060

Le SelectLe Select

JussieuJussieu

boot server

5517; 3060

Le SelectLe Select

U. Rennes IU. Rennes I

boot server

5517; 3060

Client Demo

8080Connect thru Connect thru https and https and passwdpasswd

Le SelectLe Select

IRISAIRISA

boot server

5517; 3060

FirewallFirewall

INTERNETINTERNET

FirewallFirewall FirewallFirewall FirewallFirewall

boot serverboot serverBrain MaskBET/FSL

ClassificationGM/WMVISTAL

Brain MRI (8 bits, Brain MRI (8 bits, Analyze)Analyze)

2D/3D Display(Client BrainVisa/Anatomist)

g2a

Classified Classified Volume (8 bits, Volume (8 bits,

Analyze)Analyze)

RestorationVISTAL

Head MRI (8 bits, Analyze)Head MRI (8 bits, Analyze)

a2g

Hea

d M

RI (

8 bi

ts, G

IS)

Hea

d M

RI (

8 bi

ts, G

IS)

a2ga2g

2D/3D Display(Client MRIcro/FSL)

g2a

Dat

a Fl

ow

Brain Mask (8 bits, Analyze)Brain Mask (8 bits, Analyze)

ClassificationGM/WMBALC

Restored Head MRI (8 bits, Analyze)Restored Head MRI (8 bits, Analyze)

a2g

IRM 1.5 T(8 bits, GIS)

IRM 3T(8 bits, Analyze)

RennesRennes GrenobleGrenoble

Classified Classified Volume (8 bits, Volume (8 bits,

GisGis))

Users Users applicationsapplications

Mediation Mediation servicesservices

HeterogeneousHeterogeneous& Distributed & Distributed Information Information Data BaseData Base

Internet AccessInternet Access

Common access service (query/retrieve) Common access service (query/retrieve) using the using the «« NeurobaseNeurobase »» semantic modelsemantic model

Wrapper Wrapper 11 Wrapper Wrapper ii Wrapper Wrapper nn

Uniform ViewUniform View

Information Information data base data base 11

•C++•Java•Php•.dim

Information Information data base data base ii

•Delphi•Perl•Matlab•.hdr

Information Information data base data base nn

•C•Perl•Vtk•.dcm

http://http://www.irisa.fr/visages/neurobasewww.irisa.fr/visages/neurobase

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SummarySummary: Research issues in : Research issues in NeuroinformaticsNeuroinformatics for CNS diseasesfor CNS diseases

Conception of the surgical room of the futureConception of the surgical room of the futureIntegration of intraIntegration of intra--operative multimodal sensors and effectors (e.g. robots) at operative multimodal sensors and effectors (e.g. robots) at different scales (from the molecule to the organ through the celdifferent scales (from the molecule to the organ through the cell)l)

Guidance of surgical information sources by observations and knoGuidance of surgical information sources by observations and knowledgewledge

Better understand the behavior of normal and pathological brain Better understand the behavior of normal and pathological brain systems, at different scalessystems, at different scales

Imaging the pathologies, from the organ level to the cell and thImaging the pathologies, from the organ level to the cell and the moleculee molecule

Modeling normal and pathological group of individuals (cohorts) Modeling normal and pathological group of individuals (cohorts) from image from image descriptorsdescriptors

Creation of virtual organizations of medical imagingCreation of virtual organizations of medical imaging actors thru the actors thru the dissemination of GRID and semantic web technologies in edissemination of GRID and semantic web technologies in e--health, for:health, for:

The creation of The creation of ““virtualvirtual”” cohortscohorts

The research of new specific biomarkers from imagingThe research of new specific biomarkers from imaging

Data mining and knowledge discovery from image descriptorsData mining and knowledge discovery from image descriptors

Validation and certification of new drugsValidation and certification of new drugs



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Thanks to Thanks to ……

A. A. AbadiL. L. Ait-aliC. C. AmmoniauxC. BaillardA.M. A.M. BernardA. A. BirabenA. A. BouliouB. B. Carsin-NicolC. C. CiofoloB. B. CombesI. CorougeP. P. CoupeP. P. DarnaultC. De GuibertS. S. DuchesneG. G. EdanO. El GanaouiA. A. FerialA. A. GaignardD. Garcia-lorenzoB. B. GibaudB. Godey

V. GratsacA. GrossetP. HellierP. JanninG. Le GoualherJ. LecoeurM. ManiA. MechoucheP. MeyerX. MorandiS.P. MorrisseyM. MonziolsA. OgierP. Paul

I. PratikakisE. PoiseauS. PrimaA. QuentinF. RousseauR. SeizeurL. TemalN. Wiest-Daesle

Unit/Unit/ProjetProjet VisAGeSVisAGeS, IRISA, IRISAH. Benali, Inserm, ParisY. Bizais, Inserm, Brest

P. Bouthemy, IRISA/INRIAM. Carsin, CHU

M. Chakravarty, McGillL. Collins, McGill

M. Dojat, Inserm, GrenobleJC Ferré, CHU

Y. Gandon, CHUC. Kervrann, IRISA/INRA

S. Kinkingnehun, Inserm ParisE. Leray, CHU

J. P. Matsumoto, Business ObjectsE. Mémin, IRISA/Univ. Rennes I

W. Niessen, RotterdamL. Parks, Mariarc

M. Pelegrini-Issac, Inserm, ParisP. Pérez, IRISA/INRIA

N. Roberts, MariarcY. Rolland, CHU

E. Simon, Business ObjectsD. Tosun, UCLA

P. Thomson, UCLAM. Vérin, CHU

CollaboratorsCollaborators

08/05/0808/05/08 3434

NeuroinformaticsNeuroinformatics in the context of CNS diseasesin the context of CNS diseasesC. BARILLOTC. BARILLOT

VisAGeSVisAGeS U746 INSERMU746 INSERM--INRIAINRIA

IRISA, CNRS 6074, Univ. of Rennes

Campus de Beaulieu, Rennes F-35042, FRANCE

http://www.irisa.fr/visages

Thank you for your attentionThank you for your attention

VisAGeS U746 at Short*: 1 image = 1 2D MRI slice 08/05/08 4 Neuroinformatics in the context of CNS...

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