derek hill kcl, imperial, oxford ixi.uk

Derek HillKCL, Imperial, Oxfordhttp://www.ixi.org.uk

Team

• Derek Hill, Kelvin Leung, Bea Sneller, Jinsong Ren, Julia Schnabel, Jason Harris KCL

• Jo Hajnal, Daniel Rueckert, Michael Burns, Andrew Rowland, Rolf Heckerman, Carlos Thomaz, Imperial

• Steve Smith, John Vickers, Oxford

Information eXtraction from Images (IXI)

• 3 year UK e-science project funded by core programme– Additional support from GSK, Philips

Medical Systems, Dunhill Charitable Trust

• Uses grid-enabled image registration and segmentation for drug discovery, medical research, and decision support in healthcare.

Image registration

Reference image(example slice)

Database subject image(example slice)

Brain image segmentation

Application to large cohorts

Example slicesFrom MRI Volumeimages

Research activities

• Image acquisition and analysis– Between all sites have about 100 full

time image analysis researchers (students and post-docs)

– We distribute various image analysis s/w, including image-registration.com (KCL) and FSL (from Oxford)

Why IXI?

• We call this project Information eXtraction from Images to emphasize the key concept which is using image analysis to generate image metadata – information about the images – and the generic applicability of this technology.

Why the grid?• Data grid

– Sharing distributed image databases– Enables collaborative working

• Compute grid– “on demand” computing provided by distributed

infrastructure– Users can access high performance computing when they

need it– Algorithms presented as grid services that can be combined

with workflow tools– Provenance tools (eg: Chimera) to provide “electronic paper

trail” – evolving link with Wilde/Foster Argonne National Lab

• People in “virtual organizations”– Researchers can work together more effectively– New ways for industry and academia to collaborate

Technical aims

• Scalability – To show that the grid can scale medical image analysis

to huge cohorts, using condor between sites

• Ability to share data across sites– Interoperable databases– Secure file transfer to trusted machines

• Grid services for image analysis– Wrap image analysis algorithms to create grid service

• Provenance– Keep track of how all results were obtained

• Information Extraction methodology– New algorithm that take advantage of the grid

Exemplars

• Developmental neuroimaging– Neonates from Hammersmith– Children/teens from Institute of Psychiatry

• Drug discovery– Pre-clinical brain and joint imaging

• Decision support in healthcare– Normative reference data in “dynamic brain

atlas”

• Cardiac MRI dynamic image analysis

Normative MRI reference data

• 600 normal subjects, approximately uniformly distributed between 18 and 80

• T1 volumes, multislice spin echo, [angio and DTI on sub-cohort]

• medical history questionnaire• 1.5T and 3T scanners, different vendors• Ethics approval for sharing on grid

Achievements

• Wrapping of image registration algorithms from within our consortium and also from a group at INRIA in France for demonstration of grid-enabled cross-validation of algorithms (demonstration at HealthGrid 2004,Clermont- Ferrand)

• Testbed based on XML workflow schema providing web access to grid services

• Use of IXI components to delineate talus and calcaneus from wrist to quantify disease progression in model of rheumatoid arthritis (collaboration with GSK) – Paper presented at IEEE ISBI conference, April, USA

Images onlocal client Globus

Web-based portal

Imperial Condor Cluster

Local client

INRIA MPICluster

Architecture for intraoperatible image registration (health grid demo)

IXI testbed

• Resources– 400 node sun grid engine cluster, London e-

science centre– 200 node condor installation, Imperial

College– 45 node condor installation, KCL– Distributed image database, 3 sites (MySQL

based, directly connected to MR scanners for data acquisition at 2 sites)

– globus installed at each site

IXI test bed system design

• xml schema language to describe existing image analysis applications– Defines common types, parameters, i/o of

each component, relationships between input and output

– Defines categorisation information for application discovery

– Used to construct image analysis workflows

IXI testbed Workflow Service

• OGSI compliant GT3 service, executes workflow based on xml schema

• Maps workflow to RSL specification or grid service invocation

• Handles dependencies between each workflow stage

• Tries to execute as much of workflow in parallel as possible.

IXI testbed service discovery

• OGSI based registry deployed at each site

• Users can register applications that they wish to make available to the project

• Registries aggregated to project-wide registry, which can be queried by user

IXI testbed Example Application

• demonstrator– Database can be queried for head scans (one

selected as reference) which are accessed by the workflow engine using grid-ftp

– Each head passed through workflow to extract brain

– All images aligned with reference– Atlas of variability produced– Accessible via a web server for users without

globus installed– Aim to demonstrate easy of analysis for non-

expert users.

Drug discovery with provenance

• Pharmaceutical industry in investing massively in imaging (eg: £70+m investment at Imperial announced last month)

• For drug discovery, keeping track of exactly how result were obtained is critical

• We use the Virtual Data Systems Chimera system within a web interface to do this

Application - drug discovery

• Disease model of Rheumatoid Arthritis (RA)

• Injected with disease inducing agent• MR images were acquired• Interested in talus and calcaneus• Identify them from the MR images

and study them, e.g. calculate volume to measure any erosion

Reference (atlas)image

Target image

Rigid + non-rigidregistration

Displacement field

Manual segmentation

Apply displacement field

Computed boundary of calcaneus

Segmentation Propagation

calcaneus

IXI provenance system

• Web interface wrapped around VDS, Globus Toolkit 2.4 and Condor

• Tomcat (https), VDS, Globus client, Condor on my machine– Web portal

• Globus gatekeeper, GridFTP server, Globus RLS, Condor on another machine– Storage site and execution site

• Not yet integrated with IXI testbed

My systemservices

My system

Service to delineate the calcaneus and talus from the target image

target reference_image

rigid registration

cal_seg

calcaneus cal_dof

segmentation propagation

segmentation propagation

aregdof

talus_seg

talus tal_dof

My system

My system

Jobs generated

My systemJob status in Condor

My systemClick to download files and view in vtkview

Result – intra-subject registration

Day +3

Overlay images with the computed boundaries of calcaneus highlighted

Result – inter-subject registration

Overlay images with the computed boundaries of calcaneus highlighted

Day -12

My systemService to render the surfaces of the bones

My systemJob submitted

Job status

My system

My systemBrowse all the executed services

and click on a file to view the history

My system

Provenance requirements

• Access control and security– We have some unusual

provenance requirements– Provenance information needs

access control so not everyone can see provenance of data

– We have started a collaboration with Mike Wilde and Ian Foster using our application as a use case for VDS.

Conclusions

• Medical image analysis has some characteristics that make it well suited to grid computing– Algorithms have increasing computational

complexity (> moores law)– There is a need to deal with larger data

volumes– Latency is not critical– Collaboration is essential– Regulatory environment requires good

curation and provenance