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Enabling Cloud and Grid Powered Image Phenotyping

Enabling Cloud and Grid Powered Image Phenotyping. Martha Narro iPlant Collaborative narro@iplantcollaborative.arizona.edu Adapted from slides by Nirav Merchant. Motivation. High throughput imaging is essential for large-scale phenotyping .

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Enabling Cloud and Grid Powered Image Phenotyping

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  1. Enabling Cloud and Grid PoweredImage Phenotyping Martha Narro iPlant Collaborative narro@iplantcollaborative.arizona.edu Adapted from slides by Nirav Merchant

  2. Motivation • High throughput imaging is essential for large-scale phenotyping. • Affordable robotics for image acquisition creating vast amounts of imaging data. • Many laboratories have automated imaging setups, but lack a comparable analysis platform. • Super resolution microscopy and multi-channel images are pushing the boundaries of storage and computational capabilities.

  3. Motivation II • New, improved analysis algorithms are being published. • Biologists struggle to use them. • Developers need images to test algorithms. • Scientists need to compare algorithms, reproduce results. • Metadata is key for managing large datasets. • Sharing and collaborating with large image data sets is challenging. • ONE SIZE FITS ALL APPROACH DOES NOT WORK

  4. BisqueImage Management, Analysis, Sharing System

  5. Why Bisque? • Biologists can • Manage images • Choose from multiple analysis options • Overlay results to validate findings • Annotate images • Share images, results, annotations via secure link • Algorithm developers can • Publish new analysis methods, easily make them web accessible • Produce interactive plots, visualizations using built in API • Integrated with iPlant storage and computation infrastructure for scalability

  6. How does it work? iPlant Computational Infrastructure Bisque High Bandwidth Transfer High Bandwidth Transfer iPlant Data Store

  7. Bisque Features • Web application • Tiling, zooming, step through image stacks, play as movie • Display 20K x 20K pixel images in web browser • Handles 100+ image, video formats • Import large image sets (≤ 40 GB Bisque), extremely large ones (> 40 GB iPlant Data Store) • Scale analyses using distributed computing (connected to XSEDE) and workflow engines (Pegasus, Condor)

  8. Pollen Tube Tracker Analysis Stack of time-lapse images of pollen tubes growing in vitro displaying maximum intensity in each image Tracking by Bisque Source: Ravi Palanivelu, Kobus Barnard

  9. MultiRoot Growth Analysis Time lapse image stack of seeds growing Root tip tracking by Bisque Source: Edgar Spalding

  10. Seed Size Analysis High resolution flat bed scanner image of seeds Edge detection and analysis by Bisque Source: Edgar Spalding

  11. Automated Pollen Identification Coming Attraction! Imagine some pollen grains Imagine the species of the pollen grains has been identified Source: Matina Donaldson-Matasci, et al.

  12. Users Currently iPlant has 5+ groups actively using this infrastructure • 3 Graduate courses • 2 Summer courses/workshops • NSF ADBC Thematic Collections Network(Yale University led)

  13. Users Currently iPlant has 5+ groups actively using this infrastructure • 3 Graduate courses • 2 Summer courses/workshops • NSF ADBC Thematic Collections Network(Yale University led) • Welcome, 1 Pollen RCN

  14. Bisque-iPlant Team • Bisque (U. California, Santa Barbara) • B. S. Manjunath • Kris Kvelikval • Dmitry Fedorov • Phytomorph (U. Wisconsin, Madison) • Edgar Spalding • Nathan Miller • Logan Johnson • Nirav Merchant (iPlant; U. Arizona, Tucson)

  15. Useful Links • Main application: • bisque.iplantc.org • Support: • http://ask.iplantcollaborative.org • Project Website • http://www.iplantcollaborative.org

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