Tony Pan, Ashish Sharma, Metin Gurcan Kun Huang, Gustavo Leone, Joel Saltz The Ohio State University...
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Transcript of Tony Pan, Ashish Sharma, Metin Gurcan Kun Huang, Gustavo Leone, Joel Saltz The Ohio State University...
Tony Pan, Ashish Sharma, Metin GurcanKun Huang, Gustavo Leone, Joel Saltz
The Ohio State University Medical Center, Columbus OH
gridIMAGE Microscopy: A caBIG Based System for Image Processing
and Quantitative Analysis
For more information, please contact Tony Pan ([email protected]) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu
Agenda
• Motivation
• caGrid overview
• gridIMAGE Radiology
• gridIMAGE Microscopy
• Future Directions
Digitized Microscopy: Virtual Slide Cooperative Studies
• CALGB, Children’s Oncology Group Cooperative Studies
• Roughly 30 slides/day – 30 GB/day compressed, 300GB/day uncompressed
• Remote review of slides
• Tissue bank QA/QC
• Computer assisted tumor grading
EXAMPLE: Large Scale Imaging Pipeline Con-focal Microscopy (joint work with NCMIR)
• Problem definition: how many pixels of a certain color intensity exist within a rectilinear region of interest?
• Implementation: the prefix sum solves the query without scanning every pixel within the region of interest
normalization stitching warping
thresholdingtessellationprefix sumgeneration
querying
correctional tasks
target task preprocessing tasks
declustering
Image file
What is Grid?
• A lot of different things to a lot of different people• Evolution of distributed computing to support sciences and engineering• Some common themes prevail:
– Sharing of resources (computational, storage, data, etc)– Secure Access (global authentication, local authorization, policies, trust,
etc)– Open Standards– Virtualization
• “The real and specific problem that underlies the Grid concept is coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations.”– I. Foster, C. Kesselman, S. Tuecke. International J. Supercomputer
Applications, 15(3), 2001.
• A good general overview can be found here: http://gridcafe.web.cern.ch/gridcafe/
What is caGrid?
• Development project of NCI caBIG Architecture Workspace, aimed at helping define and implement Gold Compliance
• No requirements on implementation technology will be necessary for Gold compliance– Specifications will be created defining requirements
for interoperability– caGrid provides core infrastructure, and tooling to
provide “a way” to achieve Gold compliance• Gold compliance creates the G in caBIG
– Gold => Grid => connecting Silver Systems
Benefits and Motivation
• Facilitate research and clinical decision support with large number of
datasets and multiple analysis algorithms.
– Parameter studies, clinical and preclinical trials, etc
• Enable better algorithm development and validation through the use of
many distributed, shared image datasets
• Support remote algorithm execution – reduce data transfer and avoid the
need to transmit PHI
• Reduce overall processing time and algorithm development cycle through
remote compute resource recruitment and CAD compute farms
• Scalable and open source — caGrid 1.0 based
Data and Algorithm Sharing over the Internet
gridIMAGE RadiologyExpose algorithms, human markup and
image data as caGrid Services
Image Data Service
• Expose data in PACS servers as caGrid Data Service• Open source DICOM server — PixelMed
• XML based data transfer (NCIA-like schema)
caBIG
Columbus
3 Participating Data Services
Los Angeles
CAD Application Service• caGRID middleware to wrap CAD applications with grid services• Interact with Data Services to retrieve images• Invoke algorithm with required inputs• Transform and report results to results data service
caGrid Introduce Hides complexity of plugging an algorithm into the grid
CAD algorithms provided by iCAD Inc. Prototypes for investigational use only; not commercially available
caGrid Dorian Used to provide authentication service
caBIG
Columbus
2 Participating Analytic Services
Human Markup Services• Query a work-order queue to detect any new markup requests • Interact with Data Services to retrieve images• Capture markups and save to results data service
BaltimoreColumbus
2 Human Markup Services
User Interface
Available data services
Queried results
DICOM image viewer
Click to browse images, submit CAD analysis, and view results
Technologies
• caBIG caGrid 1.0 beta– Globus Toolkit 4.0.1 compliant– Introduce toolkit for service creation and deployment– Dorian security management for user and service
authentication and authorization– CQL based query and retrieve for data services
• External applications and algorithms– Matlab– Lung Nodule CAD– etc
gridIMAGE Microscopy
• A prototype implementation to demonstrate applicability of gridIMAGE Radiology architecture for microscopy image analysis
• Liver macrophage quantification– IHC staining– Single field of view capture in
JPEG format– Matlab algorithm for
segmentation and quantification
gridIMAGE Microscopy Architecture
• The Image Data Service holds microscopy images
– caGrid Image retrieval via SOAP and Java object serialization
– Data modeled using XML schema
• Application Service– Interfaces with Matlab server to
execute algorithms– retrieves images directly from Image
Data Service
• Result handling– images are submitted back to the
Image Data Service– Return quantitative results to user
interface
• Current user interface support– Command line based invocation
currently– GUI based image review and analysis
invocation is next
MatlabAlgorithm
ImageStorage
Some Sample Results
Benefits and Motivation
• Facilitate research and clinical decision support with large number of
subjects and multiple analysis algorithms.
– Parameter studies, clinical and preclinical trials, etc
• Enable better algorithm development and validation through the use of
many distributed, shared image datasets
• Support remote algorithm execution – reduce data transfer and avoid the
need to transmit PHI
• Reduce overall processing time and algorithm development cycle through
remote compute resource recruitment and CAD compute farms
• Scalable and open source — caGrid 1.0 based
Data and Algorithm Sharing over the Internet
Future Direction
• UsabilityGUI support for microscopy image reviewWhole slide image support
• Advanced algorithmsMore real-world algorithms for real applicationsDistributed algorithms
• Location independenceMove algorithms to dataMove both data and algorithms to compute serversCurrently supported – ongoing collaborations to deploy these capabilities
• Security and PrivacyEncryption, authorization, and Just-In-Time anonymization for the image data services
• Scaling and DeploymentHigh performance image transfer mechanismsGreater number and variety of image analysis algorithms
Acknowledgements
For more information, please contact Tony Pan ([email protected]) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu
This project was funded by NIH BISTI Center for Grid Enabled Medical Imaging, NCI, NSF, and the State of Ohio
Board of Regents BRTT program