1. The Virtual Microscope Umit V. Catalyurek Department of Biomedical Informatics Division of Data Intensive and Grid Computing

2. The Virtual Microscope Joel Saltz Renato Ferreira Michael Beynon Chialin Chang Alan Sussman Tahsin Kurc Robert Miller Angelo Demarzo Mark Silberman Asmara Afework Anthony Wiegering

3. Virtual Microscope (VM) Interactive software emulation of high power light microscope for processing image datasets visualize and explore microscopy images screen for cancer categorize images for associative retrieval electronic capture of slide examination process used in resident training collaborative diagnosis Virtual Microscope (Hopkins/UMD), Distributed Telemicroscopy System (Rutgers), [Gu] Virtual Telemicroscope, Virtual Microscopy (UPMC), Baccus Virtual Microscope

4. The Virtual Microscope Data requirement Full cases consisting of multiple digitized glass slides with data acquired at 400X Single spot 1000x1000 pixels, 3-byte RGB=3MB A slide of 2.5cmx3.5cm requires 50x70 grid = 10GB uncompressed Each slide can have multiple focal planes Johns Hopkins alone generates 500,000 slides per year

5. The Virtual Microscope Client-server architecture Java 1.2 Client Portability Data storage & Image compression More efficient storage, reduced transmission time 2 server implementations: Customized instance of Active Data Repository Improved scalability, portability, user-defined processing Component-based implementation using DataCutter Heterogeneous systems, portability, user-defined processing Caching in the VM Client Improved response time Experimental Results

6. VM Client

8. Image Declustering 0 12 345 67 0 01 234 5 6 7 1 234 56 7 01 23 4 4 56 70 12 3 5 6 701 23 4 56 70 12 3 45 67 0 12 345 67

9. Image Compression JPEG compression - storage and network data reduction by a factor of 10 still may take long time to transmit images For example, 640x480 image 920 KB uncompressed ~ 90 KB jpeg compressed ~ 13 seconds to transfer using 56 Kb modem

10. Active Data Repository (ADR) A C++ class library and runtime system for building parallel databases of multi- dimensional datasets enables integration of storage, retrieval and processing of multiple datasets on parallel machines and clusters. provides support for common operations such as data retrieval, memory management, scheduling of processing across a parallel machine. can be customized for various applications. Front-end: the interface between clients and back- end. Back-end: data storage, retrieval, and processing. Distributed memory parallel machine or cluster, with multiple disks attached to each node Customizable services for application-specific processing

11. Query Interface Service Query Submission Service Front-end Virtual Microscope Front-end Dataset Service Attribute Space Service Data Aggregation Service Indexing Service Query Execution Service Query Planning Service Back-end Client... Query: * Slide number * Focal plane * Magnification * Region of interest Image blocks Virtual Microscope with ADR

12. DataCutter A suite of Middleware for subsetting and filtering multi-dimensional datasets stored in a distributed environment Indexing Service Multilevel hierarchical indexes based on spatial indexing methods – e.g., R-trees Filtering Service Distributed C++ component framework Specialized components for processing data filters – logical unit of computation, high level tasks, init,process,finalize interface streams – how filters communicate unidirectional buffer pipes uses fixed size buffers (min, good) manually specify filter connectivity and filter-level characteristics

13. Virtual Microscope with DataCutter zoomviewread_datadecompressclipclip-zoom-viewread_datadecompressdecompress-clip-zoom-viewread_data DC-5F DC-3F DC-2F

14. Caching in the Client Reduce data re-transmission Cache part of the retrieved data in the client Cache multiple resolutions/magnifications Cache only what the user views Two-level cache client memory is the first level cache local disk on the client machine is the second level

15. Caching Multiresolution Images

16. VM Server Performance

17. ADR VM Server Performance

18. VM ADR Server under workload

19. VM Servers: ADR vs DC

21. VM: ADR vs DC on SMP

22. Caching Client Performance

24. Summary 2 VM servers: Homogeneous systems tightly coupled parallel machines with attached local disks Heterogeneous systems, grid Java 1.2 Client Multiresolution image caching Try http://vmscope.jhmi.edu

25. End of Talk