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Breakout Session. Interactive Data Exploration and Visualization Exploring Large Data Sets Collaborative Control and Analysis Teaching Support Remote Consultation Surgical Planning Access Control Policies. Particpants. Kazunori Nozaki – Osaka U – Dentist, CFD

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breakout session
Breakout Session

Interactive Data Exploration and Visualization

Exploring Large Data Sets

Collaborative Control and Analysis

Teaching Support

Remote Consultation

Surgical Planning

Access Control Policies


Kazunori Nozaki – Osaka U – Dentist, CFD

Chao Quan Chen – Edinburgh Med Ultrasound Processing 300 MB

Martin Connell – Edinburgh – Visualization, Processing...

Cecilia Germain – Computer Research Lab Paris – Online Radiology Grid

Nick Avis – Cardiff U – Computational Steering, large scale viz

Daniel Hanlon – Daresbury Laboratory – Globus/grid/portals – med

Sofie Norager – EC – HealthGrid Merging Biomedical informatics

Richard Ansorge – U Cambridge – Cancer care telemedicine, viz

Steve Pieper – SPL / BIRN – Surgical Planning, Viz


Therapy Planning

Intra-operative sugical assist – interactive

IMRT (Radiation Treatement Planning) – medium term

Volume measurement – continuum from small interaction/large computation to large interaction/computation.

Interactive Rendering

Volumetric segmentation

Adaptive control of instrumentation

categories of visualization
Categories of Visualization


Simple Tabular / Histogram

Static Rendering


Large Data Model – Astro, CFD -> MEG,

Want to have

Interactive Static Data Set

2D, 3D, 4D, Multi-Modal, Populations, Multi-Scale...

Dynamic – rendered on the fly

Interactive Dynamic Data Set

Simulation/Calculation on the fly

Computational Steering

Tiled / distributed rendering

middleware requirements
Middleware Requirements


Priority Allocation

Administrative control of priories

3 Levels of task types

Low – scheduling small tasks on grid

Medium – like the scheduler itself

Large – workflow (classical grid – globus, grid services)

Quality Of Service

Auditing of resource usage

Level 2 Grid – some hardware is being dedicated to grid use, with interactive applications being allowed

enabling tools
Enabling Tools

VNC - – GLX enabled vnc with tight vnc for unix

Shared whiteboards

Videoconferencing – e.g. Tandberg

NAG Explorer – grid enabled

SGI viz server

user requirements
User Requirements

Need More Example Applications

Performance Evaluation of all levels of the programming stack

What is the overhead of Grid Services?

Methods to determine type of service you are connecting to and choose alternate implementations depending on type of service

Comparison to dedicated cluster approaches to same problems

Intelligent/Selective Data Access

Standard file system API

Move to Database as API


Migrate to new grid architecture from locally controlled machines

Perception of local control

Redundancy of compute resources provides better uptime than local machine

Market economy of resources

Comparison with other resource markets (e.g. Electrical power)

Need an overabundance of resources so that user needs can be accomodated in worst-case

What are commercial companies doing?


Grid is not ready for interactive use yet


Distributed rendering techniques for large data using grid resources

Multiple data types integrated in the rendering


2D images

3D volumes, vectors, tensors, tracts



dynamic/kinetic models – metabolism, function

Error metrics displayed in view

Data from multiple simulations, instruments

Parameter searches

projects visual semantic grid
Projects (Visual Semantic Grid!)

Queryable Visual Elements

BIRN Query Atlas Prototype – brain cortex parcelation click to launch web search

Every object is a hot link to data about itself

Metadata, provenance, source (sql or url from which it was loaded or best way to access it)

“User profiles” to steer user to types of information (e.g. students vs. clinicians)

Scalability to large numbers of users


Native language, technical vs non-technical

Local expert translators

Homologies between schools of thought (mediator)

remote resources on confidential data
Remote Resources on Confidential Data


Clinican queries PACS for longitudinal patient data and sends dicom files to grid for registration and analysis

How to keep the whole compute transaction secure even when calculations happen remotely

Also important to industrial applications (e.g. Big pharma)


More European / Asian experts could be drawn on

Physics problems are different

Better defined constraints on amount of data

Off-line computations, data reduction

Who do you trust?

How to make the social/administrative parts of the grid authentication work (e.g. account creation)

European datagrid project may have answers

paper topics
Paper Topics

Visualization Taxonomy

API types to support “weights” of processing types (batch vs. function call)

XML Schema for Semantically aware visual elements

Caching/Random Access to Data

Resource aware applications / proxy

With application override possible

what is limitation of grid
What is limitation of grid?

Justify conclusion that grid is not yet suitable for interactive visualization

What is overhead for grid services? What optimizations are possible?

What are computer center policies that limit or enable interactive applications? What are the reasons for those policies?

What hardware limitations influence ability to use grid resources interactively? Swapping?

Do the cluster OS and queing systems allow pre-emptive multitasking?


Example code of grid aware visualization

Software that can display the other testbed data

other groups
Other Groups

Visualization needed for other groups

With reference to our taxonomy

Expert review QA

Radiologist interpretation – unknown “gold standard”

paper outline for visualization
Paper Outline for Visualization



Locally+Remotely Generated Large Dataset

Remote Compute Resource Required

Secure Communications

Interactive Response / Computational Steering

Potentially Large Output to Visualize/Interpret/Archive

Radiology Specific for Clinical Research

CT/MR/US/PET... longitudinal disease tracking

5D data sets, multi-component images

Image Standardization / Calibration

Inter-, Intra-subject/modality registration

Accommodate instrument differences – seek state of the art techniques

Patient confidential information protection

Security or Deidentification


Starting pose; Non-rigid registration needs help avoiding local minima

Remote consultation and collaboration

Data streaming of local and remote data

System Requirements

On-demand computing – across weights of compute jobs (batch, in-between, interactive)

Reduce overhead for initiating grid services (authentication, resource descovery...)

Virtual Overabundance of Resources

Pool IT budgets from individual researchers and share among groups

What you need is almost always available to you


An instance of the interaction scenario either in registration or measurement


Clinical sites

Application software sites

Computer resource sites with grid infrastructure expertise and resources that can be dedicated to interaction experimentation – and with links to the grid infrastructure development groups to work on system requirements groups

(Optional for Discussion: industrial partner)

IBM, Sun, Dell...

GE, Siemens, Philips...

Big Pharma...


slicer http www slicer org
Slicer -
  • Flagship Application of the SPL
    • VTK, C++, Tcl/Tk, OpenGL
  • Modeling/Visualization Platform
  • Neuroscience Research
  • Surgical Planning
  • Source Available and Free for Non-Clinical Use
exploring large data sets
Exploring Large Data Sets
  • Example: Keyhole
    • Seamless transitions across multiple scales
    • Intuitive User Interface
    • Displays where data exists and where not
    • Integration of Imaging and non-imaging data
    • Multiple ways to find the data of interest (by feature type, name, address, interactive exploration...)
  • How to Adapt/Expand this to Medical Images?
query atlas interface project
Query Atlas Interface Project
  • An interface to interact with the BIRN database as easily as Keyhole interacts with the earth data
  • Draw on skills of SDF (award-winning information design firm) in genomics/proteomics visualization