Difference between revisions of "2010 Summer Project Week Mouse Brain Cortical Thickness Analysis"

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Image:mbrain_scale.png|[[#|Size comparison of mouse and human brains]]
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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Our method consists of three steps: segmentation, thickness measurement, and statistical analysis among experimental groups. The segmentation step provides the neo-cortex separation from other brain structures and thus is a simple preprocessing step for the thickness measurement. Our pipeline uses atlas-based segmentation, such that the structural segmentation as well as source and destination boundaries are manually defined over the atlas by our expert. The source boundary represents the neocortex surface facing the inside of whole brain and the destination boundary represents the surface facing the outside of the brain.
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* Segmentation
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** Provides the neo-cortex separation from other brain structures
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The thickness measurement step computes a Laplacian field from the source boundary toward the destination boundary. By solving forward and backward Laplacian PDE equation, each voxel is assigned two values: forward and backward distance. The forward distance is measured between the voxel and source boundary and the backward distance is measured between the voxel and the destination boundary. The thickness is defined as the sum of these two values on boundary voxels.
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** Uses atlas-based segmentation, such that the structural segmentation
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** Source and destination boundaries are manually defined over the atlas by our expert.
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To compare the thickness between different subjects, corresponding points between subjects need to be identified. This is achieved using particle correspondence algorithm minimizing entropy between dynamically moving sample points.
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* The thickness measurement step:
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** Computes a Laplacian field from the source boundary toward the destination boundary.  
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** Using measureThicknessFilter by Marc Niethammer
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* Statistical Analysis
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** Use particle correspondence algorithm minimizing entropy between dynamically moving sample points
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** ShapeAnalysis tools by Beatriz Paniagua
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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We successfully applied our methods to prenatally cocaine-exposed rat populations at PND5 and PND14, as well as to an adult mouse population that was exposed to ethanol during adolescence. In all cases, the populations were compared with matching control animals and statistical analysis was performed. Cortical thickness analysis provides valuable insight to the understanding of neurodegenerative diseases and normal neurodevelopmental processes. All our presented software tools are open-source and thus available to the research community.
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* My goal for this project was to convert the pipeline into Slicer3 wizard module
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* It was done with need of more improvements (with help of Clement):
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** Text editor or CSV editor to process streams of line data
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** Result visualization
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* I learned here:
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** Slicer/QT superbuild: a great playground for better module creation
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** CTest: the most impressive one to build robust tools
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** GIT: helpful for collaboration and maintenance
 
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Latest revision as of 13:56, 25 June 2010

Home < 2010 Summer Project Week Mouse Brain Cortical Thickness Analysis

Key Investigators

  • UNC: Ipek Oguz, Martin Styner

Objective

Cortical thickness measurement is an important tool in investigating animal models of neurodegenarative diseases. However, the difference between human and rodent models necessitates adaptation of automated image analysis tools to the rodent scale. This project aims to provide tools for mouse and rat brains equivalent to state-of-the-art human brain analysis software.

Approach, Plan

  • Segmentation
    • Provides the neo-cortex separation from other brain structures
    • Uses atlas-based segmentation, such that the structural segmentation
    • Source and destination boundaries are manually defined over the atlas by our expert.
  • The thickness measurement step:
    • Computes a Laplacian field from the source boundary toward the destination boundary.
    • Using measureThicknessFilter by Marc Niethammer
  • Statistical Analysis
    • Use particle correspondence algorithm minimizing entropy between dynamically moving sample points
    • ShapeAnalysis tools by Beatriz Paniagua

Progress

  • My goal for this project was to convert the pipeline into Slicer3 wizard module
  • It was done with need of more improvements (with help of Clement):
    • Text editor or CSV editor to process streams of line data
    • Result visualization
  • I learned here:
    • Slicer/QT superbuild: a great playground for better module creation
    • CTest: the most impressive one to build robust tools
    • GIT: helpful for collaboration and maintenance

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

  1. ITK Module
  2. Slicer Module: Thickness measurement filter developed by Marc Niethammer
    1. Built-in
    2. Extension -- python module for whole analysis pipeline
    3. Extension -- loadable
  3. Other (Please specify)

References

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