I V I F2 F July 2005 Talk

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  • I V I F2 F July 2005 Talk

    1. 1. Tony Pan , Ashish Sharma, Benjamin Rutt, Metin Gurcan, Stephen Langella, Joel Saltz, Tahsin Kurc Department of Biomedical Informatics The Ohio State University Medical Center, Columbus OH gridFTP for Third Party Data Transfer in gridImage — A Demonstration of the Middleware Project For more information, please contact Tony Pan (tpan@bmi.osu.edu) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu
    2. 2. gridFTP for Bulk Data Transfer <ul><li>A Demo from the In Vivo Imaging Middleware Project </li></ul><ul><li>Use of gridFTP for Bulk Data Transfer </li></ul><ul><li>Provide a client to support remote human markup of nodules </li></ul><ul><li>Enable better algorithm development and validation through the use of many distributed, shared image datasets </li></ul><ul><li>Support remote algorithm execution – reduce data transfer and avoid the need to transmit PHI </li></ul><ul><li>Reduce overall processing time and algorithm development cycle through remote compute resource recruitment and CAD compute farms </li></ul><ul><li>Scalable and open source — caGrid 1.0 based </li></ul>
    3. 3. Benefits <ul><li>Remote execution of multiple CAD algorithms using </li></ul><ul><li>multiple image databases </li></ul><ul><li>Facilitate research and clinical decision support with large number of subjects and multiple CAD algorithms. </li></ul><ul><ul><li>Parameter studies, clinical and preclinical trials, etc </li></ul></ul><ul><li>Provide a client to support remote human markup of nodules </li></ul><ul><li>Enable better algorithm development and validation through the use of many distributed, shared image datasets </li></ul><ul><li>Support remote algorithm execution – reduce data transfer and avoid the need to transmit PHI </li></ul><ul><li>Reduce overall processing time and algorithm development cycle through remote compute resource recruitment and CAD compute farms </li></ul><ul><li>Scalable and open source — caGrid 1.0 based </li></ul>
    4. 4. gridCAD Architecture Expose algorithms, human markup and image data as caGrid Services
    5. 5. Image Data Service <ul><li>Expose data in PACS servers as caGrid Data Service </li></ul><ul><li>An open source DICOM server — Pixelmed </li></ul><ul><li>XML based data transfer (our own schema) </li></ul>caBIG Columbus 3 Participating Data Services Los Angeles
    6. 6. CAD Application Service <ul><li>caGRID middleware to wrap CAD applications with grid services </li></ul><ul><li>Interact with Data Services to retrieve images </li></ul><ul><li>Invoke algorithm with required inputs </li></ul><ul><li>Transform and report results to results data service </li></ul>caGrid Introduce Hides complexity of plugging an algorithm into the grid <ul><ul><li>CAD algorithms provided by iCAD Inc. Prototypes for investigational use only; not commercially available </li></ul></ul>caGrid Dorian Used to provide authentication service caBIG Columbus 2 Participating Analytic Services
    7. 7. Human Markup Services <ul><li>Query a work-order queue to detect any new markup requests </li></ul><ul><li>Interact with Data Services to retrieve images </li></ul><ul><li>Capture markups and save to results data service </li></ul>Baltimore Columbus 2 Human Markup Services
    8. 8. User Interface Available data services Queried results DICOM image viewer Click to browse images, submit CAD analysis, and view results
    9. 9. caBIG Technologies <ul><li>caGrid 1.0 </li></ul><ul><ul><li>Globus Toolkit 4.0.1 compliant </li></ul></ul><ul><ul><li>Introduce toolkit for service creation and deployment </li></ul></ul><ul><ul><li>Dorian security management for user and service authentication and authorization </li></ul></ul><ul><li>caDSR </li></ul><ul><ul><li>CIP compliant vocabulary for DICOM data elements </li></ul></ul><ul><li>CQL based query and retrieve for “data services” </li></ul>
    10. 10. Future Direction <ul><li>Location independence </li></ul><ul><ul><li>Move algorithms to data </li></ul></ul><ul><ul><li>Move data to algorithms </li></ul></ul><ul><ul><li>Move both data and algorithms to compute servers </li></ul></ul><ul><ul><li>Currently supported – ongoing collaborations to deploy these capabilities </li></ul></ul><ul><li>Security and Privacy </li></ul><ul><ul><li>Encryption and Just-In-Time anonymization for the image data services </li></ul></ul><ul><li>Scaling and Deployment </li></ul><ul><ul><li>High performance image transfer mechanisms </li></ul></ul><ul><ul><li>Greater number and variety of CAD vendors </li></ul></ul><ul><ul><li>Additional application areas, including CAD for other diseases and in vitro image analysis </li></ul></ul>
    11. 11. Acknowledgements For more information, please contact Tony Pan (tpan@bmi.osu.edu) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu The RIDER dataset used during this demonstration is provided courtesy of NCI Cancer Imaging Program iCAD Inc.: Euvondia Friedmann, Maha Sallam, Tim Carter 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
    12. 12. Thank You

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