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gridIMAGE Microscopy: A caBIG Based System for Image Processing and Quantitative Analysis

gridIMAGE Microscopy: A caBIG Based System for Image Processing and Quantitative Analysis. Tony Pan, Ashish Sharma, Metin Gurcan Kun Huang, Gustavo Leone, Joel Saltz The Ohio State University Medical Center, Columbus OH. For more information, please contact Tony Pan (tpan@bmi.osu.edu)

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gridIMAGE Microscopy: A caBIG Based System for Image Processing and Quantitative Analysis

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  1. gridIMAGE Microscopy: A caBIG Based System for Image Processing and Quantitative Analysis Tony Pan, Ashish Sharma, Metin Gurcan Kun Huang, Gustavo Leone, Joel Saltz The Ohio State University Medical Center, Columbus OH For more information, please contact Tony Pan (tpan@bmi.osu.edu) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu

  2. Agenda • Motivation • caGrid overview • gridIMAGE Radiology • gridIMAGE Microscopy • Future Directions

  3. 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

  4. correctional tasks Image file normalization stitching warping declustering target task preprocessing tasks querying prefix sum generation tessellation thresholding 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

  5. 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/

  6. 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

  7. 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

  8. gridIMAGE Radiology Expose algorithms, human markup and image data as caGrid Services

  9. 3 Participating Data Services Columbus caBIG Los Angeles Image Data Service • Expose data in PACS servers as caGrid Data Service • Open source DICOM server — PixelMed • XML based data transfer (NCIA-like schema)

  10. 2 Participating Analytic Services Columbus caBIG 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 caGrid Dorian Used to provide authentication service • CAD algorithms provided by iCAD Inc. Prototypes for investigational use only; not commercially available

  11. 2 Human Markup Services Baltimore Columbus 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

  12. User Interface Available data services DICOM image viewer Queried results Click to browse images, submit CAD analysis, and view results

  13. 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

  14. 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

  15. Matlab Algorithm 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 Image Storage

  16. Some Sample Results

  17. 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

  18. 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

  19. Acknowledgements 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 For more information, please contact Tony Pan (tpan@bmi.osu.edu) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu

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