Network requirements for 3-D flying in a zoomable brain database ...
towards a zoomable cell
description
Transcript of towards a zoomable cell
towards a zoomable cell
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abstract cellnatural coordinate systemData
>48.000 3D ProteinStructures from PDB
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A IHGFB C D E
>200.000 Images from scientific publications
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Computer Graphicsand Visualization
TECHNISCHEUNIVERSITÄTDRESDEN
Zoomable CellStefan Gumhold Michael Schröder
Norbert Blenn Anne Tuukkanen
Marcel Spehr Matthias Reimann
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 3
Goals 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 10m 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
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 4
Human CellsNew Problems
Several different instances of the same typeeach instance is flexible
cells are treated badly before imagingvery different imaging modalities are used
Deformation Framework
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 5
Various Data Types
cell
nucleus
pore
complexes
proteinsprimitives, smooth surfaces
implicit surfaces
height fields
images: 2D, 3D, perspective
images: 2D, 3D, perspective
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 6
Data Augmentation 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
nucleolusenveloppore
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 7
Integration of Datasets
Segmentation
FeatureDetection Labeling
non-rigidRegistration
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 8
Deformation
reference model
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 9
Plan to a Solution
start with fully interactive toolsadd automation step by step with full
interactivity for correctionsfind features that persist over different
scalesdevelop learning based segmentation
approachesexploit mutual information to register
datasets of different dimension and modality
Computer Graphicsand Visualization
Zoomable Cell, SPP 1335, Kickoff Meeting, 9.12.08, Dagstuhl 10
Visualization Engine 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
Computer Graphicsand Visualization
Example Images
Computer Graphicsand Visualization
Query Based Exploration of Images
Available image information
• Expert labeled text (categorical)
• Unstructured information of related text (textual)
• Inherent image features (abstract description of image appearance)
More reliable and structured
Less reliable and structured
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
Hierarchical Browsing
Fish-Eye View
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
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)
Class information from Image Features
1. Definition of semantic classes (assisted and manually, Gene Ontology labels)
2. Relation of abstract image descriptors to semantic classes (training, learning)
3. Evaluation of generalization ability
GoImage – Semantic Image Search
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
GoImage – Semantic Image Search
Refinement of a search for membranes through selecting nuclear envelope p.a.