Image-Based Steering for Integrative Biology Lakshmi Sastry, Richard Wong, Helen Wright with...
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Transcript of Image-Based Steering for Integrative Biology Lakshmi Sastry, Richard Wong, Helen Wright with...
Image-Based Steering for Integrative Biology
Lakshmi Sastry, Richard Wong, Helen Wrightwith contributions from Ronald Fowler, Sri Nagella and Anjan Pakhira
Acknowledgements
Ken Brodlie and Jason Wood CompuSteer funding Integrative Biology project scientists
Image-based Steering
SimulateFilterMapRender
Visualization processingIntegrated display
X
X
Integrative Biology (IB)
Grid technology to enable in-silico experiments by computational biologists
Combined resources for computation, data management, visualization and data analysis
Focus on fatal diseases – heart and cancer
Example IB Applications
Modelling heart electrical activity during arrhythmia:
Tulane whole ventricular model – epicardial potential distribution over heart geometry during shock-induced arrhythmia
Fenton-Karma 4-variable model on 2D slice of tissue
An episode of arrhythmogenesis in ventricular model. The arrhythmia is a figure of eight reentry with one rotor on the anterior (left panel) and another on the posterior (right panel) of the ventricles. The arrows show wave propagation. The scale is saturated, potentials above 20mV are shown in red and below -90mV are shown in blue.
Example IB Applications
In vitro and in-silico models of tumour growth during very early stages
Seamless secure access to very large volumes of image data, processing, simulation and interaction will accelerate understanding of disease process.
Steering for IB Applications Complex and compute intensive with
tens and hundreds of parameters Verification of models that continue to
be refined Computational exploration of
parameter space Expanding set of simulations and
visualization toolkits
Image-based Interaction Extrinsic parameters (scalars,
vectors) mimic widgets but minimise context switching
Parameters intrinsic to the solution graphic, e.g. position specifications
The IB interface provides a layer of abstraction above the clientside libraries for computational steering.
The Case for Server-side Applications
Application may already have steering embedded
Developing a steerable interface and other scalable services for each application does not scale
Difficult to embed steering and other services into certain visualization toolkits
Users want continuity in their visualization toolkits
Minimises changes needed to application software
Client-side Consequences
Keep client generic – configure on set-up to meet application requirements
Needs to handle various geometry and image formats
Application-specific activity e.g. to resolve geometry elements or nodes, takes place server-side
IB Interface
Visualisation & interactors
panel
Control panel of widgets gViz
clientside
IB Interface
Visualisation & interactors
panel
Control panel of widgets gViz
clientside
Client A
Simulation (e.g. CARP)
gViz sim. module
Steer
View
Visualisation toolkit (e.g. Meshalyser)
IB Server
Data
Image & image based parameter values from coder/decoder
Steer
View
Client B
Collaborative gViz Overview Parameter changes are passed to all
collaborators for visibility (steer/view arrows)
Committed parameters are passed to all collaborators and the simulation, locking interactors
Arrival of data unlocks interactors – implies token-passing
Data streams – not used here – separate results from parameters
Demonstrator Elements Tumour modelling – growth of ductal
carcinoma in breast Results – time-varying tumour cell
counts in axial and radial direction of duct
Steering of nutrient consumption rate and cell-to-duct-wall slip coefficient
Utilises gViz rel.2 (collaborative) for parameter passing, calling Fortran
Visualization Back-end
IRIS Explorer, loosely coupled Simulation outputs file of results (time
step) which triggers visualization Height-field plot varying in time
height = cell numbers colour = pressure
Steering nutrient consumption and cell death rate (6MB movie)
OpenGL Interactors
Experiences
Hard to ‘wipe the slate clean’ before starting again
New collaboration helps Mode is ‘extreme collaboration’ (cf.
extreme programming) Needs dedicated time Trips - how long is ‘just long enough’?
Remaining Question Marks
Token maintenance over the various architecture pathways
Recombination of 3rd party geometries/images with interactors Anticipate little problem for extrinsics Intrinsics more difficult
gViz and multiple simulations?
Remaining Question Marks
How scalable is the architecture really?
Will scientists and steering libraries ever really mix?
What support do scientists need to use steering libraries – documentation, examples, GUI builders?