derek hill kcl imperial oxford http www ixi org uk n.
Skip this Video
Loading SlideShow in 5 Seconds..
Derek Hill KCL, Imperial, Oxford PowerPoint Presentation
Download Presentation
Derek Hill KCL, Imperial, Oxford

Derek Hill KCL, Imperial, Oxford

114 Views Download Presentation
Download Presentation

Derek Hill KCL, Imperial, Oxford

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Derek Hill KCL, Imperial, Oxford

  2. 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

  3. 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.

  4. Image registration Reference image (example slice) Database subject image (example slice)

  5. Brain image segmentation

  6. Application to large cohorts Example slices From MRI Volume images

  7. 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)

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Architecture for intraoperatible image registration (health grid demo) Web-based portal Local client INRIA MPI Cluster Images on local client Imperial Condor Cluster Globus

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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

  21. 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

  22. Segmentation Propagation Rigid + non-rigid registration calcaneus Target image Reference (atlas) image Displacement field Apply displacement field Computed boundary of calcaneus Manual segmentation

  23. 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

  24. My system services

  25. target reference_image rigid registration aregdof talus_seg cal_seg segmentation propagation segmentation propagation tal_dof talus calcaneus cal_dof Service to delineate the calcaneus and talus from the target image My system

  26. My system

  27. My system Jobs generated

  28. My system Job status in Condor

  29. Click to download files and view in vtkview My system

  30. Result – intra-subject registration Day +3 Overlay images with the computed boundaries of calcaneus highlighted

  31. Result – inter-subject registration Day -12 Overlay images with the computed boundaries of calcaneus highlighted

  32. Service to render the surfaces of the bones My system

  33. My system Job submitted Job status

  34. My system

  35. and click on a file to view the history My system Browse all the executed services

  36. My system

  37. 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.

  38. 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