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 (tpan@bmi.osu.edu) 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 (tpan@bmi.osu.edu) 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