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Visual Brain Mapping for Structural Informatics: A Geometric Constraint Approach

Explore the visualization-based brain mapping project led by Jim Brinkley at the University of Washington. This project aims to build an information framework for brain mapping using a distributed, web-accessible system optimized for language mapping. The focus is on reconstructing the brain surface accurately for surgical applications, with approaches like skull-stripping and shape-based modeling. The project involves different components such as structural components, shape components, and constraint networks to represent and propagate shape information efficiently.

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Visual Brain Mapping for Structural Informatics: A Geometric Constraint Approach

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  1. Visualization-based Brain Mapping Jim Brinkley (PI) Structural Informatics Group Dept Biological Structure University of Washington, Seattle Funded by the Human Brain Project NIDCD NIMH

  2. Goals • An information framework for brain mapping • Organized around structure • Spatial • Symbolic • Implemented in a distributed, web-accessible system • Optimized for language mapping

  3. Photo

  4. Visual Brain Mapper (VBM)

  5. Requirements for visualization-based brain mapping • Accurate reconstruction of brain surface as seen at neurosurgery • Different problem than surface reconstruction for unfolding, flattening, analysis

  6. Reconstructing the surface • Isosurface following • Needs cleanly segmented cortex: the skull-stripping problem

  7. Approaches to skull-stripping • Manual • Deformable models • 3-D region growing/mathematical morphology

  8. A shape-based approach • Represent “knowledge” of expected shape and range of variation of cortical envelope • Use to guide the search for gray-CSF boundary • Infer the boundary where none is present • Allow user corrections

  9. Representing shape • Geometric constraint networks • Set of local constraints • Interact together to generate a global shape description

  10. Constraint Network Vi (Vi1, Vi2,…) Cij Vj (Vj1, Vj2,…)

  11. Geometric Constraint Network Vi (Vi1, Vi2,…) Cij Vj (Vj1, Vj2,…)

  12. Radial contour modelStructural component Ri [Ri,inner … Ri,outer ] Cij Rj [Rj,inner, Rj,outer ]

  13. Radial contour modelShape component Ri Cij = [Ri/Rjmin … Ri/Rjmax] Rj

  14. Constraint Propagation

  15. Radial surface model

  16. Radial surface model neighbors

  17. Shape-based skull stripping • A module of VBM • Load previously trained shape model • Load MRI, MRV, MRA volumes • Find cortical envelope • Mask skull • Find cortical surface • Find veins and arteries

  18. Mid-sagittal plane

  19. Anterior Commissure

  20. Posterior commissure

  21. Cortical bounding box

  22. Initial model

  23. Initial model - end-on

  24. Initial model - lateral view

  25. Propagation from poles

  26. Define one radial

  27. Propagation from one radial

  28. Error

  29. Correct error

  30. Generate mask

  31. Generate mask

  32. Error in mask

  33. Generate mask

  34. Generate isosurface

  35. Extract veins

  36. Mask veins

  37. Vein isosurface

  38. Extract arteries

  39. Mask arteries

  40. Artery isosurface

  41. Vessels

  42. All

  43. Map

  44. Select Model Select Patient Workflow Find Envelope Define Landmarks Align Propagate & Search Mask Volume Segment Cortex Segment Veins Iso- Surface Segment Arteries Map

  45. Select Patient Workflow Architecture Align Segment Cortex Segment Veins Segment Arteries Map

  46. End User Web Interfaces Authoring Programs Brain Map Visualizer Experiment Manager Analyzer Visual Brain Mapper Symbolic Model Builder Internet Graphics Server Repository Manager Knowledge Server Structural Information Servers Spatial Knowledge Symbolic Knowledge 3-D Maps Images 3-D Models Metadata Spatial Information Symbolic Information

  47. Kevin Hinshaw Jeff Prothero Andrew Poliakov Richard Martin David Corina Ken Maravilla George Ojemann Rex Jakobovits Kate Mulligan Hector Lettich Linda Shapiro Cornelius Rosse Contributors

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