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Derek Hill KCL, Imperial, Oxford 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.

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derek hill kcl imperial oxford http www ixi org uk
Derek Hill

KCL, Imperial, Oxford

  • 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
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
Image registration

Reference image

(example slice)

Database subject image

(example slice)

application to large cohorts
Application to large cohorts

Example slices

From MRI



research activities
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 (KCL) and FSL (from Oxford)
why ixi
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
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
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
  • 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
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
  • 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

Architecture for intraoperatible image registration (health grid demo)



Local client



Images on

local client

Imperial Condor Cluster


ixi testbed
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
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
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
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
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
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
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
segmentation propagation
Segmentation Propagation

Rigid + non-rigid



Target image

Reference (atlas)


Displacement field

Apply displacement field

Computed boundary of


Manual segmentation

ixi provenance system
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 system
My system


my system1



rigid registration












Service to delineate the calcaneus

and talus from the target image

My system
my system3
My system

Jobs generated

my system4
My system

Job status in Condor

result intra subject registration
Result – intra-subject registration

Day +3

Overlay images with the computed boundaries

of calcaneus highlighted

result inter subject registration
Result – inter-subject registration

Day -12

Overlay images with the computed boundaries

of calcaneus highlighted

my system7
My system

Job submitted

Job status

my system9

and click on a file to view the history

My system

Browse all the executed services

provenance requirements
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.
  • 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