Tony Pan, Ashish Sharma, Metin Gurcan Kun 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 Dept. of Biomedical Informatics, The Ohio State University

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 normalizationstitchingwarping thresholdingtessellation prefix sum generation querying correctional tasks target taskpreprocessing 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), A good general overview can be found here:

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 Radiology Expose 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 Baltimore Columbus 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 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 Matlab Algorithm Image Storage

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 Usability GUI support for microscopy image review Whole slide image support Advanced algorithms More real-world algorithms for real applications Distributed algorithms Location independence Move algorithms to data Move both data and algorithms to compute servers Currently supported – ongoing collaborations to deploy these capabilities Security and Privacy Encryption, authorization, and Just-In-Time anonymization for the image data services Scaling and Deployment High performance image transfer mechanisms Greater number and variety of image analysis algorithms

Acknowledgements For more information, please contact Tony Pan Dept. of Biomedical Informatics, The Ohio State University 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