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towards a zoomable cell

towards a zoomable cell. A. B. C. D. E. F. G. H. I. natural coordinate system. Data. abstract cell. >200.000 Images from scientific publications. ?. >48.000 3D Protein Structures from PDB. Zoomable Cell. Stefan Gumhold Michael Schröder Norbert Blenn Anne Tuukkanen

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towards a zoomable cell

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  1. towards a zoomable cell A B C D E F G H I natural coordinate system Data abstract cell >200.000 Images from scientific publications ? >48.000 3D ProteinStructures from PDB

  2. Zoomable Cell Stefan Gumhold Michael Schröder Norbert Blenn Anne Tuukkanen Marcel Spehr Matthias Reimann

  3. Data analysis Natural coordinate system (NCS) Mapping of images from literature to NCS 3D models of complexes in NCS Visualization aggregation of images, volumes and 3D models Rendering across scale from 10m to 1Å Natural adjustment of visualization parameters with dynamic labeling HCI support for Virtual Reality environments speech control and input device development flexible navigation community support through web integration Impact Interface life scientists „from different scales“ data aggregation and analysis platform production of illustrative materials Goals Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl3

  4. New Problems Several different instances of the same type each instance is flexible cells are treated badly before imaging very different imaging modalities are used Deformation Framework Human Cells Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl4

  5. Various Data Types • cell • nucleus • pore • complexes • proteins images: 2D, 3D, perspective images: 2D, 3D, perspective height fields implicit surfaces primitives, smooth surfaces Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl5

  6. define reference models for each dataset scale imaging modality features points curves regions labeling of features for pairs of datasets feature mapping additional alignment information nucleolus envelop pore Data Augmentation Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl6

  7. Integration of Datasets Segmentation FeatureDetection Labeling non-rigidRegistration Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl7

  8. Deformation reference model Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl8

  9. Plan to a Solution • start with fully interactive tools • add automation step by step with full interactivity for corrections • find features that persist over different scales • develop learning based segmentation approaches • exploit mutual information to register datasets of different dimension and modality Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl9

  10. protein structures primitive splatting tubes, surfaces deferred shading sorting based transparency 3d surface models LOD based rendering depth peeling based transparency Images & Volumes volume rendering compression transfer functions Visualization Engine Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl10

  11. Example Images

  12. Query Based Exploration of Images

  13. Available image information More reliable and structured Less reliable and structured Expert labeled text (categorical) Unstructured information of related text (textual) Inherent image features (abstract description of image appearance)

  14. Navigation/Exploration • Around 100.000 images currently available to us • Even with automatic analysis one needs supporting browsing techniques • If we have features that measure appropriate image similarities: • Hierarchical Browsing • Fish-Eye View

  15. Hierarchical Browsing

  16. Fish-Eye View

  17. Methods to structure image data set • By hand • Automatic analysis (off-the-shelf methods) • Unsupervised (Clustering) • Supervised (Multiclass Support Vector machines) • Need for appropriate problem oriented feature set

  18. Image Feature Definition Vast numbers of image descriptors are available Need for general purpose image descriptors because of wide variety of image origins Standardized Multimedia content description (MPEG-7)

  19. Class information from Image Features Definition of semantic classes (assisted and manually, Gene Ontology labels) Relation of abstract image descriptors to semantic classes (training, learning) Evaluation of generalization ability

  20. Comprehensive protein-interaction mapping projects underway What is the cost of completing an interactome map and what is the best strategy for minimizing the cost? How can quality and coverage of interaction data be maximized? GoImage – Semantic Image Search

  21. GoImage – Semantic Image Search

  22. GoImage – Semantic Image Search

  23. Refinement of a search for membranes through selecting nuclear envelope p.a.

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