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e-Infraestructuras Nacionales
BIMCV: The Perfect "Big Data" Storm.
Maria de la Iglesia, PhD. http://ceib.san.gva.es
Collision of Peta Bytes of Population Image Data, Millions of Hardware Devices and Thousands of Software Tools.
OVERVIEW
• Big Data
• Strategic Vision of Big Data in EU
• Strategic Vision of Big Data in US
• Big Data in Neuroimaging
• Population Imaging
• EuroBioimaging – BIMCV – Valencia Node
• Neuroimaging
• Relevant facts
Big Data
Big data techniques and technologies
• Techniques for analyzing big data
– A/B testing.
• Association rule learning.
• Classification.
• Cluster analysis.
• Crowdsourcing.
• Data fusion and data integration. – Signal processing
– natural language processing
• Data mining.
Big data techniques and technologies
• Techniques for analyzing big data
– Ensemble learning
– Genetic algorithms
– Machine learning
– Natural language processing (NLP)
– Neural Networks
• Pattern recognition
– Network analysis
Big data techniques and technologies
• Techniques for analyzing big data
– Optimization
• Pattern recognition
• Predictive modeling.
• Regression.
• Signal processing– time series analysis
– data fusion
• Spatial analysis.
• Statistics.
Big data techniques and technologies
• Big DataTechnologies– Big Table. (Proprietary distributed database system
built on the Google File System. Inspiration for Hbase)
– Business intelligence (BI). BI tools are often used to read data that have been previously stored in a data warehouse or data mart
– Cassandra. An open source (free) database management system designed to handle huge amounts of data on a distributed system. This system was originally developed at Facebook and is now managed as a project of the Apache Software foundation
Big data techniques and technologies
• Big DataTechnologies– Cloud computing. – Data mart. – Data warehouse. using ETL (extract, transform, and load) – Distributed system. – Dynamo – ETL– Google File System. – Hadoop– HBase. – MapReduce. – Mashup
Big data techniques and technologies
• Big DataTechnologies– Non-relational database.
– R.
– Relational database.
– Semi-structured data.
– SQL.
– Stream processing.
– Structured data.
– Unstructured data.
– Visualization.
Big data techniques and technologies
• Big DataTechnologies
– VISUALIZATION
• Tag cloud
• Clustergram
• History flow
• Spatial information flow
VISUALIZATION: Tag cloud
VISUALIZATION: Clustergram
VISUALIZATION: History flow
VISUALIZATION: Spatial information flow
Strategic Vision of Big Data in EU
How Is the Europe Union Responding? In Big Data
Panel: Personalized Medicine in the Era of Big Data
EHTEL Symposium
Tapani Piha
• Head of Unit for eHealth and Technology Assessment
European Commission
DG Health and Consumers
Health Systems and Products
How does Big Data link to the Personalized Medicine?
•Big Data refers to a collection of data sets so large and complex, it’s impossible to process them with the usual databases and tools
•The data is gathered (most of the time) by people just living their lives (e.g. using mobile phones, the internet, driving cars, paying with banking cards)
•Big data is used in the private sector (e.g. Google), and in the public sector (e.g. NSA)
Big Data use in public health & health care?
•Research: "In the last five years, more scientific data has been generated than in the entire history of mankind”1
•Health care: more evidence about personalized treatment, better selection of right provider, better equipped health care providers (e.g. IBM's Watson)
•Public health: better personalized life-style info for citizens, earlier detection of epidemics, more and quicker access to epidemiological information
12012 Winston Hide, The Promise of Big Data, Harvard Public Health
Commission action on Big Data
•BIG-project: multi-sectorial initiative started in 2011 to promote adoption of earlier waves of big data technology and contribute to EU competitiveness;
•Green paper on mHealth: to assess market and further clarify what is needed in the legal framework concerning mHealth
•Study in health program: to assess the usages and adoption of big data programs for (public) health systems within the EU.
Strategic Vision of Big Data in US
How Is U.S. Responding? National Institute of Standards an
Technology (NIST)
NIST is an agency of the U.S. Department of Commerce.
To search federal science and technology web sites, including online databases see: science.org
NIST program questions:Public Inquiries Unit: (301) 975-NIST (6478), Federal Relay Service (800) 877-8339 (TTY). NIST, 100 Bureau Drive, Stop 1070, Gaithersburg, MD 20899-1070Technical website questions: [email protected]
NIST Big Data Public Working Group
Big Data PWG Overview Presentation
September 30, 2013
Wo Chang, NIST
Robert Marcus, ET-Strategies
Chaitanya Baru, UC San Diego
NBD-PWG Overview9/30/13
Agenda
• Why Big Data? Why NIST?
• NBD-PWG Charter
• Overall Workplan
• Subgroups Charter and Deliverables
– Use Case and Requirements SG
– Definitions and Taxonomies SG
– Reference Architecture SG
– Security and Privacy SG
– Technology Roadmap SG
• Next Steps
28
NBD-PWG Overview9/30/13
Why Big Data? Why NIST?
• Why Big Data? There is a broad agreement among commercial, academic, and government
leaders about the remarkable potential of “Big Data” to spark innovation, fuel commerce,
and drive progress.
• Why NIST? (a) Recommendation from January 15 -- 17, 2013 Cloud/Big Data Forum and (b)
A lack of consensus on some important, fundamental questions is confusing potential users
and holding back progress. Questions such as:
– What are the attributes that define Big Data solutions?
– How is Big Data different from the traditional data environments and related
applications that we have encountered thus far?
– What are the essential characteristics of Big Data environments?
– How do these environments integrate with currently deployed architectures?
– What are the central scientific, technological, and standardization challenges that
need to be addressed to accelerate the deployment of robust Big Data solutions?
NBD-PWG is being launched to address these questions and is charged to develop
consensus definitions, taxonomies, secure reference architecture, and technology roadmap
for Big Data that can be embraced by all sectors.
29
NBD-PWG Overview9/30/13
NBD-PWG Deliverables
Working Drafts version 1.0 for
1. Big Data Definitions
2. Big Data Taxonomies
3. Big Data Requirements
4. Big Data Security and Privacy Requirements
5. Big Data Architectures White Paper Survey
6. Big Data Reference Architectures
7. Big Data Security and Privacy Reference Architectures
8. Big Data Technology Roadmap
30
NBD-PWG Overview9/30/13
NBD-PWG Workplan
31
Big Data Ecosystem in One Sentence
• Use Clouds running Data Analytics Collaboratively processing Big Data to solve problems in X-Informatics ( or e-X)
• X = Astronomy, Biology, Biomedicine, Business, Chemistry, Climate, Crisis, Earth Science, Energy, Environment, Finance, Health, Intelligence, Lifestyle, Marketing, Medicine, Pathology, Policy, Radar, Security, Sensor, Social, Sustainability, Wealth and Wellness with more fields (physics) defined implicitly
• Spans Industry and Science (research)
• Education: Data Science see recent New York Times articles
• http://datascience101.wordpress.com/2013/04/13/new-york-times-data-science-articles/
32
Social Informatics
Visual&DecisionInformatics
33
Big Data Definition
• More consensus on Data Science definition than that of Big Data
• Big Data refers to digital data volume, velocity and/or variety
that:
– Enable novel approaches to frontier questions previously inaccessible or
impractical using current or conventional methods; and/or
– Exceed the storage capacity or analysis capability of current or
conventional methods and systems; and
– Differentiates by storing and analyzing population data and not sample
sizes.
– Needs management requiring scalability across coupled horizontal
resources
34
Vendor-neutral and Technology-agnostic Proposals
Data Processing FlowM0039
Data Transformation FlowM0017
IT StackM0047
35
Data Processing FlowM0039
Data Transformation FlowM0017
IT StackM0047
36
Vendor-neutral and Technology-agnostic Proposals
Data Processing FlowM0039
IT StackM0047
Data Transformation FlowM0017
37
Vendor-neutral and Technology-agnostic Proposals
Vendor-neutral and Technology-agnostic
Proposals
Data Transformation FlowM0017
IT StackM0047
Data Processing FlowM0039
38
Electronic Medical Record (EMR) Data I
• Application: Large national initiatives around health data are emerging, and
include developing a digital learning health care system to support
increasingly evidence-based clinical decisions with timely accurate and up-
to-date patient-centered clinical information; using electronic observational
clinical data to efficiently and rapidly translate scientific discoveries into
effective clinical treatments; and electronically sharing integrated health
data to improve healthcare process efficiency and outcomes. These key
initiatives all rely on high-quality, large-scale, standardized and aggregate
health data. One needs advanced methods for normalizing patient,
provider, facility and clinical concept identification within and among
separate health care organizations to enhance models for defining and
extracting clinical phenotypes from non-standard discrete and free-text
clinical data using feature selection, information retrieval and machine
learning decision-models. One must leverage clinical phenotype data to
support cohort selection, clinical outcomes research, and clinical decision
support.
40
PP, Fusion, S/Q, Index Parallelism over EMR (a set per person), viewersStreaming
Electronic Medical Record (EMR) Data II
• Current Approach: Clinical data from more than 1,100 discrete logical,
operational healthcare sources in the Indiana Network for Patient Care
(INPC) the nation's largest and longest-running health information
exchange. This describes more than 12 million patients, more than 4
billion discrete clinical observations. > 20 TB raw data. Between
500,000 and 1.5 million new real-time clinical transactions added per
day.
• Futures: Teradata, PostgreSQL and MongoDB supporting information
retrieval methods to identify relevant clinical features (tf-idf, latent
semantic analysis, mutual information). Natural Language Processing
techniques to extract relevant clinical features. Validated features will
be used to parameterize clinical phenotype decision models based on
maximum likelihood estimators and Bayesian networks. Decision
models will be used to identify a variety of clinical phenotypes such as
diabetes, congestive heart failure, and pancreatic cancer.
41
Pathology Imaging/ Digital Pathology I• Application: Digital pathology imaging is an emerging field where examination of high
resolution images of tissue specimens enables novel and more effective ways for
disease diagnosis. Pathology image analysis segments massive (millions per image)
spatial objects such as nuclei and blood vessels, represented with their boundaries,
along with many extracted image features from these objects. The derived information
is used for many complex queries and analytics to support biomedical research and
clinical diagnosis.
42
MR, MRIter, PP, Classification Parallelism over ImagesStreaming
Pathology Imaging/ Digital Pathology II• Current Approach: 1GB raw image data + 1.5GB analytical results per 2D image. MPI for
image analysis; MapReduce + Hive with spatial extension on supercomputers and
clouds. GPU’s used effectively. Figure 3 of section 2.12 shows the architecture of
Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial
analytics for analytical pathology imaging.
43
• Futures: Recently, 3D pathology imaging
is made possible through 3D laser
technologies or serially sectioning
hundreds of tissue sections onto slides
and scanning them into digital images.
Segmenting 3D microanatomic objects
from registered serial images could
produce tens of millions of 3D objects
from a single image. This provides a
deep “map” of human tissues for next
generation diagnosis. 1TB raw image
data + 1TB analytical results per 3D
image and 1PB data per moderated
hospital per year.Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging
18: Computational Bioimaging
• Application: Data delivered from bioimaging is increasingly automated, higher
resolution, and multi-modal. This has created a data analysis bottleneck that, if
resolved, can advance the biosciences discovery through Big Data techniques.
• Current Approach: The current piecemeal analysis approach does not scale to
situation where a single scan on emerging machines is 32TB and medical
diagnostic imaging is annually around 70 PB even excluding cardiology. One
needs a web-based one-stop-shop for high performance, high throughput
image processing for producers and consumers of models built on bio-imaging
data.
• Futures: Goal is to solve that bottleneck with extreme scale computing with
community-focused science gateways to support the application of massive
data analysis toward massive imaging data sets. Workflow components include
data acquisition, storage, enhancement, minimizing noise, segmentation of
regions of interest, crowd-based selection and extraction of features, and
object classification, and organization, and search. Use ImageJ, OMERO,
VolRover, advanced segmentation and feature detection software.
44
MR, MRIter?, PP, Classification Parallelism over ImagesStreaming
22: Statistical Relational Artificial Intelligence for Health Care
• Application: The goal of the project is to analyze large, multi-modal medical data
including different data types such as imaging, EHR, genetic and natural language. This
approach employs the relational probabilistic models that have the capability of
handling rich relational data and modeling uncertainty using probability theory. The
software learns models from multiple data types and can possibly integrate the
information and reason about complex queries. Users can provide a set of descriptions
– say for instance, MRI images and demographic data about a particular subject. They
can then query for the onset of a particular disease (say Alzheimer’s) and the system
will then provide a probability distribution over the possible occurrence of this disease.
• Current Approach: A single server can handle a test cohort of a few hundred patients
with associated data of 100’s of GB.
• Futures: A cohort of millions of patient can involve petabyte datasets. Issues include
availability of too much data (as images, genetic sequences etc) that complicate
analysis. A major challenge lies in aligning the data and merging from multiple sources
in a form that can be made useful for a combined analysis. Another issue is that
sometimes, large amount of data is available about a single subject but the number of
subjects themselves is not very high (i.e., data imbalance). This can result in learning
algorithms picking up random correlations between the multiple data types as
important features in analysis.45MRIter, EGO Parallelism over People and their EMRStreaming
PREDICTIVO PREVENTIVO PERSONALIZADO PARTICIPATIVO
El paradigma P4 de la Medicina
V-OLUME V-ARIETY V-ELOCITY V-ALUE
El paradigma V4 en Big Data Medicina
Big Data en Neuroimagen
human neuroimaging is now, officially, a “big data” science
• Among the examples of “big data” featured at the meeting was – no surprise - human neuroimaging
• The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative
• Initiatives surrounding large-scale brain mapping are also underway in Europe http://www.humanbrainproject.eu
• Organization for Human Brain Mapping (OHBM; http://www.humanbrainmapping.org)
How Big is “Big”?
• While size is a relative term when it comes to data, medical imaging applied to the brain comes in a variety of forms which each generating differing types and amounts of information about neural structure and/or function.
• NeuroImage, indicates that since 1995 the amount of data collected has doubled approximately every 26 months. At this rate, by 2015 the amount of acquired neuroimaging data alone, discounting header information and before more files are generated during data processing and statistical analysis, may exceed an average of 20GB per published research study
Growth of Neuroimaging Study Size
0
5000
10000
15000
20000
1990 1995 2000 2005 2010 2015 2020
Me
gaB
yte
s
Year
Expected
Observed
Predicted
Van Horn and Toga (in press) Brain Imaging and Behavior
Kryder’s law: Exponential Growth of Data
VOLUME OF DATA
MB = MEGABYTE = 106, GB = GIGABYTE = 109
TB = TERABYTE = 1012, PB = PETABYTE = 1015
COMPUTE
POWER
CPU TRANSISTOR
COUNTS
MOORE’S LAW
YEARS
SINGLE CRYO BRAIN VOLUME
1600 CM2
NEUROIMAGING
(ANNUALLY)
GENOMICS
(BP/YR)
Voxel Resolution Gray Scale Color 200 GB 10 MB 1x105 1985-1989
Size Count 8bits 16bits 24bits 1 TB 100 MB 1x106 1990-1994
1cm 12x15x9 1620 3000 4860 50 TB 10 GB 5x106 1995-1999
1mm120x
150x90
1.62
MB3.24 MB 4.86 MB 250 TB 1TB 1x107 2000-2004
100 µm1200x
1500x900
1.62
GB3.24 GB 4.86 GB 1 PB 30TB 8x106 2005-2009
10 µm
12000x
15000x
9000
1.62
TB3.24 TB 4.86 TB 5 PB 1 PB 1x109 2010-2014
1 µm
120000x
150000x
90000
1.62
PB3.24 PB 4.86 PB 10+ PB 20+ PB 1x1011 2015-2019
(estimated)
Big Neuroimaging + Big Genetics =REALLY Big Data
• With the ability to obtain genome-wide sets of single nucleotide polymorphism (SNP) information becoming routine and the costs of full genomic sequencing rapidly becoming affordable.
• Next Generation Sequencing (NGS) methods, for major brain imaging studies such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Weiner, Veitch et al. 2012), with its initially available sample of 832 subjects.
• As the bond between neuroimaging and genomics grows tighter, with both areas growing at incredible rates, disk storage, unique data compression techniques
Multisite Consortia andData Sharing
• Examples of multisite neuroimaging efforts can be found in the ubiquitous application of neuroimaging in health but also in devestating illnesses such as:
• Parkinson’s (Evangelou, Maraganore et al. 2009)
• psychiatric disorders (Schumann, Loth et al. 2010)
• the mapping of human brain connectivity (Toga, Clark et al. 2012
• databases of aging and aging-related diseases, largescaleAutism Research (NDAR; Hall,Huerta et al. 2012) and the Federal Interacgency Traumatic Brain Injury Research (FITBIR; Bushnik and Gordon 2012)
Multisite Consortia and Data Sharing
• The various “grass roots” collections of resting-state fMRI data maintained as part of the
“1000 Functional Connectomes” project
• http://fcon_1000.projects.nitrc.org/
(see Biswal, Mennes et al. 2010)
• Task-based OpenfMRI http://www.openfmri.org(Poldrack, Barch et al. 2013) are other notable examples.
The Role of Cyberinfrastructure
• Individual desktop computers are now no longer suitable for analyzing potentially petabytesworth of brain and genomics data at a time.
• While the National Science Foundation (NSF) has made major investments in the computer architecture needed for physics, weather, and geological data.
• Eg. XSEDE, https://www.xsede.org/ , and Open Science Grid, https://www.opensciencegrid.org
The Role of Cyberinfrastructure
• The Neuroimaging Informatics Tools and Resources Clearinghouse
(NITRC; http://www.nitrc.org )
• The International Neuroinformatics Coordinating Facility (INCF; http://incf.org )
Have begun to deploy local clusters with Amazon EC2 server technology toward this goal but a larger effort will be required involving dedicated processing centers or distributed grids of linked compute centers.
Many 1,000’s of Software Tools
• Biomedical Imaging– There are 100’s of different types of image
processing algorithms and filters
– For each type of process there may be dozens of
concrete software products (instance implementations)
• (Example) Neuroimaging– NITRC lists > 500 openly shared software tools
– For each openly shared tool there may be dozens of
proprietary or less commonly used analogues
• Acquisition, processing, storage/DB, service, migration, mining, analysis, visualization, annotation, … “(data-driven) process understanding”
Millions of Dispersed Hardware Devices
• Cisco: "By the end of 2012, the number of mobile-connected devices will exceed the number of people on Earth”
• There will be over 10 billion mobile-connected devices in 2016; i.e., there will be 1.3 mobile devices per capita– These include phones, tablets, laptops, handheld gaming consoles, e-readers,
in-car entertainment systems, digital cameras, and “machine-to-machine modules”
• DBs, Clients, Servers, Compute-Nodes, Web-Services, Interfaces, …• Solution …
Dinov et al., BMC 2011
Image spatial
alignment
Slicetiming
adjustment
Statisticalmodeling(e.g. GLM)
Functional –structural
co-registration
Raw fMRI time series
High-resolutionanatomical image
Standardizedbrain atlas template
Imagesmoothing
Gaussianspatial filtering
Experimentaldesign matrix
Study Meta DataScanner protocolsSubject demographicsStimulus timingetc.
Spatialnormalizationto atlas space
Statistical results maps
Table ofstatistically significant voxels in atlasspace coordinates
Graphicaloverlays
Van Horn et al., Nature Neuro, 2004
Pipeline Version 5.9.1 FeaturesGraphical Programming Environment
11/17/2014 64
Perfect Neuroimaging-Computation Storm?
• Single Subject Studies (N=1)– Genetics:
• Depending on Coverage(X)• Whole Genome Seq Data > 320GB (>80X)• Require 2+ TB RAM, and 100+ hrs CPU
– Imaging: • Depending on protocols• 40-512 gradient directions Diffusion imaging data• Raw (multimodal) Neuroimaging Data > 10 GB• Derived Data > 100 GB• Require 100GB RAM and 70+ hrs CPU
• Large Subject Studies– Cohort studies (N>10, Typically N~100’s)– Multi-Institutional Population-wide Studies (N>1,000)– Longitudinal (neuroimaging) studies …
From Biomedical Challenges to Modeling, Computation, Tools and Curricular
Training • Quantitative Volumetric and Surface based Stats Analyses
– Interactome: Challenge↔Models↔Data Analysis↔Computation↔Education
– Statistics Online Computational Resource Che, et al., JSS (2009) No effectMarginalSignificant
Grid & Cloud Computing
• UCLA Grids
Cerebro Medulla 1,200 cores
1.4TB RAM
12,000 jobs/day
700 users
• Amazon Cloud– EC2 (Elastic Cloud Computing)
– S3 (Simple Storage Service)
• UC Grid
• Globus GridFTP
• INI Cluster @ USC– 3328 cores, 128GB RAM per 16 cores, 26tb aggregate
memory space. Connectivity is 5Gbit per 16 cores, roughly 4terabit aggregate on comp and another 4.3Tbit on the storage. 2.43PB of online storage with over 50TB of SSD accelerating it currently.
4,300 cores
9.6 TB RAM
(new)
Neuroimaging Applications: 56-ROI Global Shape Analysis (NC vs. IBS/Pain) Group
EffectsData Workflow Protocol Results
Structural T1 dataNC IBS
221 107
Mean-Curvature between-groupdifferences in:
L_cuneus
R_angular_gyrus
Left View
Right View
LeftAnteriorInsula
Neuroimaging Applications: Stat Mapping of Cortical GM Thickness (Group Effects)
DataWorkflow Protocol
Structural T1 dataCortical Models
1.0
0.0
P-v
alu
e
Results
Pipeline User Community
Population Imaging
Big Data y el sector de la Salud en Imagen Poblacional
• Según Bonnies Feldman “el potencial de Big Data en medicinareside en la posibilidad de combinar los datos tradicionales conotras nuevas formas de datos, tanto a nivel individual comoPoblacional”
• El potencial del Big Data indica que se pueden producir ahorros enel sector sanitario a través de varias vías:– Transformación de datos en información.
– Apoyo al autocuidado de las personas.
– Aumento del conocimiento.
– Concienciación del estado de salud.
• El Big Data es una metodología de acceso abierto para integrardiferentes tipos de datos en imagen poblacional, cuantificación deimagen y extracción de características.
Tipos de Estudios
• Individual
• Longitudinal
• Transversal
0 1 2 M
Estudios Poblacionales
• Estudios Poblacionales– Si no se forman grupos en la población, se calcula la media
del parámetro o parámetros.
– Si se forman grupos (control y Patológicos) se debe realizar
un contraste de hipótesis.
• Modelado Poblacional– Modelar la degeneración volumétrica de sustancia gris y
sustancia blanca
– Establecer parámetros de degeneración
– Contrastar el estado de un individuo con respecto a dicho
modelo.
Aplicación en Alzheimer
Aplicación a Casos Reales
Resultados de parámetros globales
Aplicación a Casos Reales
Resultados de grosor y volumen por estructura, junto con los valores de referencia
Aplicación a Casos Reales
• Representación de la diferencia del volumen en comparación con la población
¿Porqué no podemos combinarBELLEZA Y CIENCIA?
Objetivos BIMCV
• Desarrollar e implementar estrategias para
prevenir o tratar efectivamente las
enfermedades mediante una infraestructura de
investigación en imagen asociada a grandes
estudios poblacionales de imagen.
– Concepto de “Population Imaging”.
• Proporcionar datos,
herramientas y recursos de
proceso para realizar estudios
avanzados en imagen.
volBrain system
volBrain pipeline
Segmentación no supervisada de Glioblastomas
GIBI230
Luis Martí-BonmatíFernando ApariciAlexandre Pérez
Roberto Sanz
Carlos InfantesJose María Salinas
Cayetano Hernández
NEuro-Bioimaging VLCMariam de la Iglesia
IBIMEJuan M García-Gómez
Elies FusterJavier Juan-Albarracín
BIMCV
Nodo Valenciano
Euro-BioImagingInfraestructura Europea para la Investigación en
Tecnologías de Imagen Biomédica e Imagen Biológica.
Un proyecto sobre la hoja de ruta de las ESFRI en infraestructuras de investigación
www.eurobioimaging.eu
• Established in 2006
• Staff: 4.5, incl. 3 Project Managers, 1 assistant
• Provision of services to Network Members + EIBIR bodies
• Monitoring European Affairs + research funding opportunities
• Project management and coordination
• Information activities and media work
• Promotion of Network Membership
• Website and data base updates
• Congress activities
• Scientific Advisory Board
EIBIR key facts and daily work
In the service of research, EIBIR offers to its Network Members:
- Multidisciplinary networking- Project Management- Research communication- Research Training- Meeting organisation
EIBIR Office
88
Cronología & Financiación
• Evaluación & selección de nodos.
• Construccion de los nodes.
Financiado por los Estados Miembros (¿MINECO?)
Fase de Construcción
2013 - 2017
• Framework
• Definición de los criterios de elegibilidad para los nodos
• Llamada a los Nodos, Abierta.
Financiado por CE
Fase Preparatoria
2010 - 2013
• Acceso y formación• Tecnología y evaluación
para mejorar el servicio
Fase Operacional
………
Financiado por los Estados Miembros & EC
2017 - ….
MULTIMODAL TECHNOLOGY
NODE
Imaging Infrastructure with open user access
FLA
GSH
IP N
OD
E
FLA
GSH
IP N
OD
E
FLA
GSH
IP N
OD
E
FLA
GSH
IP N
OD
E
USE
R T
RA
ININ
G STAFF TR
AIN
ING
Web-access portal
European life scientists as users
Data storage and analysis infrastructure
User returns with results for publication
HU
BN
OD
ES
MULTIMODAL TECHNOLOGY
NODE
1st Open Call
Euro-BioImaging Nodes – Expression of Interest
The 1st Open Call: 1 February – 30 April 2013
• Multi-Modal Molecular Imaging• Phase contrast Imaging• High-field MRI• MR-PET • Population Imaging• Data Infrastructure: Challenges Framework
• The biological imaging community will call for EoIs in 6 technologies
Nodo Valenciano, BIMCV
Resultados 1ª Convocatoria
Biological Imaging
Biomedical Imaging
9 NODOS ESPAÑOLES
– 18 Instituciones –
MEDICAL IMAGING DATA BANK (BIMCV)Expresion of Interest: Population Imaging
BIG DATA DIASEASE SIGNATURESSINGLE TECHNOLOGY FLAGSHIPS
CONSORTIUM
Evaluation summary and Final ranking
• The node develops and provides access to a large database of
imaging data and the associated clinical data records.
• Big Data repository from hospitals in the Valencia region (5 million
inhabitants living over an area of 23.255 Km2. average number of
5.3 million clinical cases per year, from 210 different imaging
modalities).
• The access to such data and tools will be an efficient way of
advancing population imaging studies and research.
• The node has ability to incorporate data from other facilities
Services offered by the node
• BIMCV facility provides a multi-level and multi-ology storage
service (Vendor Neutral Archive).
• CEIB-CS node integrates access to high-performance
computational services from local and European
infrastructures (Principe Felipe Research Centre & UPV-I3M
Infrastructure).
• Open access methodology to integrate different data types for
population imaging, quantitative resources and feature
extraction.
• Comprehensive user training
Other facilities
Single Technology Flagship Node – Population Imaging: Valencia
MEDICAL IMAGING DATA BANK (BIMCV)
Services offered by the node:
• BIMCV facility provides a multi-level and multi-ologystorage service (Vendor Neutral Archive).
• CEIB-AVS node integrates access to high-performance computational services from local and European infrastructures (Principe Felipe Research Centre & UPV-I3M Infrastructure).
• Open access methodology to integrate different data types for population imaging, quantitative resources and feature extraction.
• Comprehensive user training.
BIG DATA DIASEASE SIGNATURES
Evaluation summary and Final ranking:
• Requires minor improvements (training plan, actually corrected).
• The node develops and provides access to a large database of imaging data and the associated clinical data records.
• Big Data repository from hospitals in the Valencia region (5 million inhabitants living over an area of 23.255 Km2. average number of 5.3 million clinical cases per year, from 210 different imaging modalities).
• The access to such data and tools will be an efficient way of advancing population imaging studies and research.
• The node has ability to incorporate data from other facilities.
Nodo Valenciano, BIMCV
Centro de Excelencia en Imagen Biomédica de la Conselleria de Sanitat
Sede CEIB clínica Sede CEIB computo
Servicios
With an Architecture Well Define
Other facilities
Neuroimaging. The landscapes' of the mind
Human Neuroimaging as a “Big Data” Science
http://prezi.com/sseievn7ujcf/?utm_campaign=share&utm_medium=copy
The mind landscapes
Estudio de la estructuraMorfometría
Estudio de la estructuraTractografía
Estudio de la Función
Estudio de la Función
RESTING STATE
Conectomica
Conectomica
Acknowledgment
Relevant facts
10 K Structural Modeling in Neuroimage of Valencia Region
• Dos becas de la Subdirección General de Sistemas parala Salud de la CS. Ingenieros Informáticos o Ingenierosde Telecomunicaciones (DOGV 9-07-2014).• Se van a medir las estructuras principales del cerebro.• En colaboración con LABMAN• En colaboración con Brain Dynamics• La universidad del Sur de California (Jack Van Horn)• Posiblemente con IBIME (volBrain system)
Prototipo de realidad Virtual AumentadaARiBraiN3T (Para android)