Dojat, Michel

English

Archive Ouverte en Sciences de l'Information et de la Communication

Sharing Data and Image Processing Pipelines: 
The Information Analysis & Management 
initiative
Michel Dojat

GIN
a center dedicated to 
fundamental and clinical research 
in the field of neurosciences
OUR MISSION
Understanding the brain 
and developing innovative therapies
for nervous system diseases
OUR STRATEGY
A research continuum from molecules, 
cells to small animals and humans for the 
study of neurological, neuromuscular and 
psychiatric pathologies
OUR STRENGTHS
/ Molecular and cellular biology of the 
neuronal cytoskeleton
/ Predictive models of cerebral pathologies
/ Mapping of neural networks and their 
alteration in neuropsychiatric disorders
/ Functional neurosurgery and deep brain 
stimulation
/ Functional MRI coupled with 
electrophysiology
UP TO 250 NEUROSCIENTISTS
/ 39 Researchers
/ 19 Clinicians
/ 55 Postdocs and PhD 
Students
/ 34 Research Professors
/ 57 engineers and 
technicians
Engineers
& technicians
Research
professors
(UGA – CHU)
Researchers
Clinicians
Postdocs
& PhD Students
RESEARCH TOPICS AND EXPERIMENTAL APPROACHES
/ Pre-clinical and clinical research
Developing tools and concepts 
Close links with networks such as 
GREEN, Neuropsynov, NeuroCoG...
/ Innovative technologies and 
treatments
Multidisciplinary approaches 
including human social sciences, 
and methodological developments 
(optogenetics, reconstruction of 
neural networks, 
electrophysiology...)
/ Fundamental neurosciences
Cytoskeleton, Intracellular traffic, 
Synaptic plasticity, Mechanisms
studied in normal and pathological
conditions (neurobiological diseases, 
neurodegenerative diseases, myopathies)
Sharing Data and Image Processing Pipelines: 
The Information Analysis & Management 
initiative
/347
Many Good Reasons for Sharing 
Data and Tools in In Vivo Imaging 
• Scientific
« Power failure: why small sample size undermines the reliability of neuroscience »
Button et al. Nat. Neurosc 2013
- Underpowered is endemic (false positive, false negative)
- Data analysis and reporting are often selective and biased
Wicherts et al. PLOS One 2011
- Rarely replicated so false solutions persist
Context
/34
Old fashion
fMRI
2015: Mean 28.5 
[Poldrack Nat Rev Neurosc 2017]
Context
Large cohort, Re-analysis, Replication, MetaAnalysis
« collective mind » Fox et al. Ann Rev Neurosci 2014
« crowd science » Franzoni & Suerman Res Policy 2014
/349
• Economic
Data Acquisition: e.g. neuroimaging 300€/H 30 subjects x 2 groups 18 K€ …  
Many efforts for image processing tools development and validation
dont replicate existing data and tools ….
Select the best processing pipelines for a target application
Reducing cost doing science, maximize investment
• Ethic
Healthy subject and patients give their consent for contributing to knowledge 
advancement.
For preclinical studies, ethical obligation to ensure that the animals are not wasted.
Maximize their participation
Many Good Reasons for Sharing 
Data and Tools in In Vivo Imaging 
Context
/34
Paradigm Shift: BIG DATA, OPEN SCIENCE + AI
10
Dermatologists vs CNN
127463 biopsies for training 
1942 for validation
Inception v3 (GoogleNet, using several replicas on Nvidia Titan X Gpu)
[Esteva et al Nature 2017]
Ivy Gliobastoma atlas
41 patients, 42 tumors, 440 tissue blocks, 270 
transcriptomes
11500 annotated H&E images
23000 ISH images (400 Gb/image), 400 MRI scans
[Puchalski et al Science 2018]
Cardiovascular risks from retinal fundus photos
284335 patients (EuePACS + UKBiobank)
12096 and 999 for validation
Inception v3 (GoogleNet, using several replicas on Nvidia Titan X Gpu) 
22 millions of parameters to be estimated
[Poplin et al Nat Biomed Eng 2018]
Context
/34
New (BIG DATA) model of neuroscience
11
courtesy of Mike Hawrylycz
How from the activity of millions of neurons distributed in several brain areas emerge
simple percepts and how they are linked to emotion, motivation or action?
Context
/3412FLI
France Life Imaging (FLI)
Infrastructure project for a coordinated and harmonized 
network of in vivo imaging in France.
• to coordinate research activities and combine the skills to 
push the current technological barriers
• to provide scientists a complete range of imaging 
technologies (150 imaging systems) and integrated 
services. 
• open to collaborations with industrial partners.
• complements France BioImaging for in-vitro Imaging (FBI), 
the French contribution to EuroBioImaging on the ESFRI 
roadmap. 
• 37 M€
/3413
0
5
10
15
20
25
30
35
40
45
50
IRM MEG-
EEG
Optique PET Rayons X SPECT US EPR Cyclotron ESRF 32 % Clinical
68 % Preclinical
France Life Imaging (FLI)
FLI
/3414
6 physical nodes
+1 transversal node for Information Analysis 
and Management
FLI-IAM: Information Analysis & Management
• Christian BARILLOT
FLI-IAM Chair (Rennes)
• Michel DOJAT
FLI-IAM co-Chair (Grenoble)
• Michael KAIN
FLI-IAM Technical Manager 
(Inria)
The Numeric infrastructure of FLI
(5 M€)
FLI-IAM
/3415
FLI-IAM: The Numeric Infrastructure
Our Vision
=> Targeted Users
• The Clinician: to conduct large and/or clinical and preclinical research in 
vivo imaging studies 
• The Pharma: to provide pharma and CRO companies high technological 
computational solutions in in vivo imaging
• The medical imaging community: to allow experimentation and 
validation of new innovative in vivo imaging solutions
FLI-IAM
/3416FLI-IAM
Final Report
Project IAM
Prepared for:
Michael Kain
France Life Imaging (FLI)  
IAM - Technical Manager
Mai 2017
FLI-IAM Market Study
/3417FLI-IAM
Tele-IDIs
Segments Number
Clinical imaging 4
Preclinical imaging 6
Clinical Research  
Organization 6
DSI 4
R&D Pharma 3
Software solutions 7
TOTAL 30
Interviews
30 persons 
/3418FLI-IAM
o Delocalized and virtual storage and archival solution
o Shared at a national level
o Gathering clinical (anonymized) data, imaging 
data  & meta-data
o Certified by regulatory agencies 
(e.g. CNIL, ANSM, GDPR, …)
o Manage various formats (Dicom, Nifti, Bids, …)
o Guarantee:
– Data security
– Stability of the structure
– Heath data hosting certification
– Independence from services providers
– Governmental approval of the structure(trustee)
Concept testing
Your ideal solution
/3419FLI-IAM
o Target: Academic Users
o FLI-IAM improves multi-centre studies:
– Large medical imaging repositories
– Sharing preclinical data
– These repositories allow:
• Evaluation of image processing software
• Large statistical studies
– Added value: a catalogue of certified software
o Preclinical imaging (small animal), 
neurology and oncology seem the more interesting
applications (large datasets generation)
Operational considerations
At start, institutional researchers as clients
/34
FLI-IAM: A two-steps process
20FLI-IAM
• Initial stage (2013-2017)  => 15 engineers
• Set up of the infrastructure hardware & software : specifications 
function of various categories of users, roadmap 
• Pilot phase on targeted in vivo imaging application for clinical and 
preclinical research
• Operational stage (2018 and onward)
• Provide an operator
• Extend the infrastructure to broaden the range of clinical and 
preclinical research application domains
• Extend the range of software integration by providing open 
interoperability standards with conformal statements
/3421FLI-IAM
FLI-IAM: The Numeric Infrastructure
Strengths: Based on high technological expertise and 
experience 
• From data management solutions 
• From medical image processing solutions
• From large national clinical cohorts: CATI, OFSEP, …
/3422FLI-IAM
H2020
Shanoir Archi Med
CATI-
DB
Derived 
DB
Retrieve
Query
Submission
R
egister
Standalone 
Compute/Display
FLI@Computing
Store
VIP  
Workflow
Management
FLI-IAM 
App Store
H
PC FLI-IAM
Computing 
Components
FLI-IAM image 
processing client
Ex
ec
ut
e
XDS/XCA
Information
System and
Search Engine
Metadata
Catalogs
Single Sign O
n 
Auth. service
API CARMIN
FLI-IAM
Web
portal
FLI-IAM
Distributed 
Image Archive
Black
Board
FLI-IAM: The Numeric Infrastructure
FLI@interoperability
/3423FLI-IAM
FL
I@
Co
m
pu
teFLI-IAM	
Web	portal
XDS/XCA
Information
System and
Search Engine
Metadata
Catalogs
FLI@Interoperability FLI@compute
FLI@Access
Shanoir
(instance 1)
Private
servers
(Psychiatry)
Onto
Neurolog
LAN
ArchiMed Private
servers
(Oncology)
LAN
Private
servers
ShanoirSA
(instance 2)
(Preclinic)
Onto
Neurolog_ext
LAN
CATIdb
Private
servers(Memory)
OntoCATI
LAN
(MOTEUR)
European Grid
Infrastructure
VIP
OntoVIP
PC
MedInria
(ITK)
(BrainVisa)
BRAINVisa
PC
CATI (capsul)
Private
clusters
(BrainVisa)
LAN
Black
Board
FLI-IAM: The Numeric Infrastructure
/3424FLI-IAM
• Interoperability with national & international (Xnat) existing 
solutions
• Link with VIP for pipeline execution
• Link with local environments 
• Portal for Data & Tool execution
• H2020 OpenAIRE-connect for Open science
• Two MICCAI-challenges: MRI for MS and PET for oncology
• Small animal platform
FLI-IAM: Key Milestones
/3425FLI-IAM
FLI-IAM: Challenge MICCAI 2016 MS Segmentation
• Data
• 53 patients (4 different MR scanners @ 1.5 & 3T)
• Modalities : 3DFLAIR, T2/DP, 3D T1-w, 3D T1-Gado
• 7 manual annotations per patient (>370 annotations overall)
• Two groups of data: 15 training et 38 testing
• Raw and pre-processed data available
• Registration, Denoising, Inhomogeneity correction, Brain segmentation/masking
• 13 Challengers worldwide 
(machine/deep learning + model-based)
Commowick,	O.	et	al.	Nature Scientific Reports, 2018
/34
FLI-IAM: For Preclinical Studies
26FLI-IAM
§ FLI : 68 % preclinical studies
§ Similar needs that for human studies
§ Still in infancy
§ => Appning WS (Paris, June 2018)
§ Bruker equipment
Ø Dicom + Json converter
Ø Ontoneurolog extension
/3427FLI-IAM
FLI-IAM:  Relaxometry Reference Maps
2 data provider centers
(20 rats x2) 7T
3 fitting pipelines
2 multi-atlas pipelines
T1 maps in 29 regions 
Deruelle et al submitted
/3428FLI-IAM
FLI-IAM:  Relaxometry Reference Maps
6 data provider centers (60 mice)
4.7 T, 7T, 9.4 T, 11.7T, 17.2 T
2 multi-atlas pipelines
/3429FLI-IAM
• Provide the control over sharing your data
• Manages your study meta-data and preclinical images
• Pathology models, therapies, anaesthetics and physiological data
• Import Bruker and DICOM file formats 
• Secures online data sharing and data reuse
• Provides 10 year storage in the cloud
• Processes your preclinical images on high performance systems
• Supports you to integrate your  data analysis pipeline 
• Enriches DOIs to Open Access, e.g. OpenAIRE
FLI-IAM: For Preclinical Studies
/34
FLI-IAM: Major points
30
• Integration of large data sets for population-wide studies
and the construction of imaging cohorts
• Validation and quality control of image processing tools on
reference datasets
• Reuse and sharing of image processing pipelines on
different datasets to improve research productivity and
reproducibility
• Validation of research results based on the proofed control
statistical analysis of images for quality control of
experimental research
/34
FLI-IAM: Infrastructure Deployment + Services offer
31FLI-IAM
40 M€ 4 y
300 M€ 4 y / center
3IA
/3432FLI-IAM
Scientific Committee
FLI-IAM: A collaborative action
Working Groups (Interoperability, Image 
processing pipelines, Preclinical imaging)
FLI-IAM
/34FLI-IAM
Thank You
contact-fli-iam@inria.fr


Sharing Data and Image Processing Pipelines: The Information Analysis & Management initiative

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Sharing Data and Image Processing Pipelines: The Information Analysis & Management initiative

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Archive Ouverte en Sciences de l'Information et de la Communication