Derek Hill
KCL, Imperial, Oxford
http://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 slices
From MRI
Volume
images
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 gridenabled cross-validation of algorithms
(demonstration at HealthGrid 2004,ClermontFerrand)
• 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)
Web-based
portal
Local client
INRIA MPI
Cluster
Images on
local client
Globus
Imperial Condor
Cluster
IXI testbed
• Resources
– 400 node sun grid engine cluster, London escience 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 projectwide 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 nonexpert 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
Segmentation Propagation
Rigid + non-rigid
registration
calcaneus
Target image
Reference (atlas)
image
Displacement field
Apply displacement field
Manual segmentation
Computed boundary of
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 system
services
Service to delineate the calcaneus
and talus from the target image
My system
target reference_image
rigid registration
aregdof
talus_seg
cal_seg
segmentation
propagation
talus
tal_dof
segmentation
propagation
calcaneus
cal_dof
My system
My system
Jobs generated
My system
Job status in Condor
Click to download files
and view in vtkview
My system
Result – intra-subject
registration
Day +3
Overlay images with the computed boundaries
of calcaneus highlighted
Result – inter-subject
registration
Day -12
Overlay images with the computed boundaries
of calcaneus highlighted
Service to render the surfaces
of the bones
My system
My system
Job submitted
Job status
My system
all the executed services
My Browse
system
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