Supporting Phenotyping through Visualization and Image Analysis

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Supporting Phenotyping through Visualization and
Image Analysis

• Raghu Machiraju,
• Computer Science Engineering, Bio-Medical
  InformaticsThe Ohio State University

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About Myself

• Associate Professor, Computer Science and
  Engineeering, BioMedical Informatics
• 7th Year at OSU
• Research Interests Imaging, Graphics and
  Visualization
• Notable Points
• Co-Chair of Visualization 2008 Conference,
  Columbus OH
• Alumni in video gaming/animation industry
  (Pixar, EA), National Government Labs (Lawrence
  Livermore), Industrial Research (Samsung, IBM,
  Mitsubishi Electric), Medical Schools (Harvard
  Medical School)

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Research Activities

• Medical, Biological Imaging and Visualization
• Optical Microscopy
• In-vivo, fluorescence imaging
• Structural/Functional Magnetic Resonance Imaging
• Diffusion Tensor Imaging
• Mostly interested in
• Segmentation, Registration, Tracking
• Applications phenotyping, longitudinal studies

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Reconstruction of Microscopic Architecture
Stained (HE) Light Microscopy Stack
Confocal Microscopy Stack
Cellular structures near mammary gland of a
female mouse Source Dr. Leone, Cancer Genetics,
OSU
Embryonic Structure of Zebra Fish, Source Dr.
Sean Megason, Harvard Medical School
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My Colleagues
Kishore Mosaliganti, 5th yearBioinformatics/Cance
r Genetics
Gustavo Leone, Mike Ostrowski Human Cancer
Genetics Program
Kun Huang, Biomedical Informatics
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The Usual Imaging Pipeline
Harvest Rb- Rb mice
Sectioning - 5 microns
Imaging
Visualization
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An Advanced Role for Imaging Support

• Mouse Placenta
• Role of Rb tumor suppressor gene
• Changes in placental morphology
• Fetal death and miscarriages
• Large data size
• High resolution image (1 GB)
• 8001200 slides/dataset
• Quantification
• Surface area/volume of different tissue layers
• Infiltration between tissue layers

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Need More - Morphometric Differences
Labyrinth-Spongiotrophoblast Interface
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Wild Type (Top) vs. Mutant (Bottom)
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Yet Another (A)Typical Example ?

• Mouse Mammary Gland
• PTEN phenotyping
• Data characteristics
• High resolution 20X images (1 GB)
• 500 slides/dataset
• Mammary duct segmentation and 3D reconstruction

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Digging In - Tumor Micro-Environment

• Mouse Mammary Gland
• More comprehensive system biology study
• Data characteristics
• Confocal, multi-stained
• 50 slides/dataset
• Multi-channel segmentation and 3D reconstruction

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The Last One - Zebrafish Embryogenesis
Final 3D segmentation
A 2D image plane

• Identifying and tracking development in the
  embryo
• Presence of salient structures
• 3D cell segmentations and tracking required
• Different in-plane and out-plane resolutions
• 800 Time steps available

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The Underlying Premise

• Is there an unified way to visualize and analyze
  the various microscopic image modalities ?

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The Essentials Of Microstructure

• Premise - you can measure, visualize and analyze
  cellular structures if you characterize and build
  virtual microstructure
• Component
• Distributions
• Packing
• Arrangements
• Material Interfaces

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Essential I- Component Distributions Packing

• Tissue layers differ in spatial distributions
• Characteristic packing of RBCs, nuclei, cytoplasm
  - phases
• Differ in porosity, volume fractions, sizes and
  arrangement
• NOT JUST ANOTHER TEXTURE !
• Use spatial correlation functions !

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Essential II - Component Arrangements

• Arrangements
• Complex tessellations which can better
  characterize changes.
• A step ahead of looking at only nuclei their
  packing
• Complex geometry
• Concentric arrangement of epithelial cells
• Torturous 3D ducts and vasculature

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Essentials III Material Interfaces
Labyrinth-Spongiotrophoblasts Interface
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The Holy Grail Virtual Cellular Reconstructions
Before using cellular segmentation
Using N-pcfs and cellular segmentations
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Pipelines
1 TeraByte
1Gb x 1 Gb x 900 20 x magnification
Image Registration (3-D alignment)
Feature extraction
Image Segmentation
3-D Visualization
Quantification
NIH Insight Tool Kit (ITK), NA-MIC Tools
(microSlicer3)
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Conclusions

• Highly multi-disciplinary approach.
• Need scalability and robustness
• Useful workflows need to be constructed
• Much application-domain knowledge has to be
  embedded in algorithms
• Validation of methods and proving robustness is a
  pre-occupation.
• The final goal of a virtual cellular architecture
  is not that elusive ?

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Destroying The Amazon Rain Forest ?

• K. Mosaliganti and R. Machiraju et al. An Imaging
  Workflow for Characterizing Phenotypical Change
  in Terabyte Sized Mouse Model Datasets. Journal
  of Bioinformatics, 2008 (to appear)
• K. Mosaliganti and R. Machiraju et al.
  Visualization of Cellular Biology Structures from
  Optical Microscopy Data. IEEE Transactions in
  Visualization and Computer Graphics, 2008 (to
  appear)
• K. Mosaliganti, R. Machiraju et al. Tensor
  Classification of N-point Correlation Function
  features for Histology Tissue Segmentation.
  Journal of Medical Image Analysis, 2008 (to
  appear)
• K. Mosaliganti and R. Machiraju et al.
  Geometry-driven Visualization of Microscopic
  Structures in Biology. Workshop on
  Knowledge-Assisted Visualization, Proceedings of
  EuroVis2008 (to appear).
• K. Mosaliganti, R. Machiraju et al. Detection
  and Visualization of Surface-Pockets to Enable
  Phenotyping Studies. IEEE Transactions on
  Medical Imaging, volume 26(9), pages 1283-1290,
  2007.
• R. Sharp, K. Mosaliganti et al. Volume Rendering
  Phenotype Differences in Mouse Placenta
  Microscopy Data. Journal of Computing in Science
  and Engineering, volume 9 (1), pages 38-47, Jan/
  Feb 2007.
• P. Wenzel and K. Mosaliganti et al. Rb is
  critical in a mammalian tissue stem cell
  population. In Journal of Genetics and
  Development, volume 21 (1), pages 85-97, Jan
  2007.
• K. Mosaliganti and R. Machiraju et al. Automated
  Quantification of Colony Growth in Clonogenic
  Assays. Workshop on Medical Image Analysis with
  Applications in Biology, 2007, Piscatway,
  Rutgers, New Jersey, USA.
• R. Ridgway, R. Machiraju et al. Image
  segmentation with tensor-based classification of
  N-point correlation functions. In MICCAI Workshop
  on Medical Image Analysis with Applications in
  Biology, 2006.
• O. Irfanoglu, K. Mosaliganti et al. Histology
  Image Segmentation using the N-Point Correlation
  Functions. International Symposium of Biomedical
  Imaging, 2006.

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Acknowledgements

• Joel Saltz, BMI
• Richard Sharp, Okan Irfanoglu, Firdaus Janoos,
  CSE OSU
• Weiming Xia, Sean Megason, Harvard Medical school
• Jens Rittscher, GE Global Research
• NIH, NLM Training Grant
• NSF ITR grant

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Thank You !

• Questions ?