1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 1
08/05/0808/05/08 11
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)
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 2
08/05/0808/05/08 33
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
08/05/0808/05/08 44
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 3
08/05/0808/05/08 55
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
08/05/0808/05/08 66
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
Probability of Probability of Activation YActivation Y
Hybrid RegistrationHybrid Registration
Hybrid Matching Hybrid Matching to Reference to Reference SulcusSulcus and and
BrainBrain
Probability of Probability of SulcusSulcus XX
Probability of Probability of Activation YActivation Y
+
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 4
08/05/0808/05/08 77
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]
08/05/0808/05/08 88
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] [Media 04]03] [Media 04]
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 5
08/05/0808/05/08 99
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
08/05/0808/05/08 1010
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] [Media 04]03] [Media 04]
V1
V2d
V2vV3v
V3vV3A
V4Collaboration INSERM GrenobleCollaboration INSERM Grenoble
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 6
08/05/0808/05/08 1111
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]
08/05/0808/05/08 1212
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]
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 7
08/05/0808/05/08 1313
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
08/05/0808/05/08 1414
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 8
08/05/0808/05/08 1515
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
08/05/0808/05/08 1616
Preoperative Planning :Preoperative Planning :Anatomical and Anatomical and Functional MappingFunctional Mapping
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 9
08/05/0808/05/08 1717
ImageImage--Guided Neurosurgery:Guided Neurosurgery:Interventional procedure (Interventional procedure (NeuronavigationNeuronavigation))
3D referential system
Patient
3D localizer
Surgical Microscope3D workstation
08/05/0808/05/08 1818
Brain ShiftBrain Shift
Adding observations during Adding observations during surgery: Video reconstructionsurgery: Video reconstruction
Video Based 3D reconstructionVideo Based 3D reconstruction
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 10
08/05/0808/05/08 1919
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 :
08/05/0808/05/08 2020
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 11
08/05/0808/05/08 2121
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
08/05/0808/05/08 2222
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©
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 12
08/05/0808/05/08 2323
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
08/05/0808/05/08 2424
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 13
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
08/05/0808/05/08 2626
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 14
08/05/0808/05/08 2727
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)),, ……
08/05/0808/05/08 2828
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 15
08/05/0808/05/08 2929
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
08/05/0808/05/08 3030
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 16
08/05/0808/05/08 3131
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
08/05/0808/05/08 3232
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
1st CRM-INRIA-MITACS Meeting 08/05/08
Christian BARILLOT 17
08/05/0808/05/08 3333
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