Difference between revisions of "2011 Winter Project Week:UncertaintyVisualization"

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(Created page with '__NOTOC__ <gallery> Image:PW-SLC2011.png|Projects List Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus…')
 
 
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<gallery>
 
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Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
 
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
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Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
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Image:CompositePostOp3.png|Composite image where the marginal distributions of deformed fibertracts and fMRI activated regions are visualized..
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Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
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Image:fMRIColormapAndQuintiles.png|The marginal distributions of a deformed fMRI activated region is visualized.
 
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</gallery>
  
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==Instructions for Use of this Template==
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==Visualizing Registration Uncertainty==
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#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
 
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#Copy the entire text of this page into the page created above
 
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#Link the created page into the list of projects for the project event
 
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#Delete this section from the created page
 
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#Send an email to tkapur at bwh.harvard.edu if you are stuck
 
  
 
==Key Investigators==
 
==Key Investigators==
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* SPL: Petter Risholm, William Wells
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* Utah: Tom Fletcher, Ross Whitaker
 
  
 
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<h3>Objective</h3>
 
<h3>Objective</h3>
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We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.
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We have developed a probabilistic non-rigid registration framework where we
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characterize the posterior distribution over deformations with a MCMC method.
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In practice, a large number of deformations are drawn from the posterior distribution.
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From the set of deformation samples, we can estimate the most likely deformation as well as the uncertainty of the deformation.
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The objective for the Project Week is to developing a command line module that
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can take the large set of deformation samples and generate useful marginal summarizes/visualizations of the registration uncertainty, e.g. marginal distribution of a biopsy point after registration.
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</div>
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<div style="width: 27%; float: left; padding-right: 3%;">
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<h3>Approach, Plan</h3>
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Our approach for summarizing and visualizing registration uncertainty is described in [Risholm 2010].  
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Our plan for the project week is to incorporate the generation of marginal probability maps of points and surface models as a command line module in Slicer3.
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</div>
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<div style="width: 40%; float: left;">
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<h3>Progress</h3>
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We have developed a:
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#A VTK class for generating marginal probability maps of deformed surface models.
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#A command line module for running the code through Slicer3.
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</div>
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</div>
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<div style="width: 97%; float: left;">
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==Delivery Mechanism==
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This work will be delivered to the NA-MIC Kit as a  
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#ITK Module
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#Slicer Module
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##Built-in
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##Extension -- commandline Yes
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##Extension -- loadable
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==References==
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*Risholm P, Samset E, Pieper S, Wells W [http://www.spl.harvard.edu/publications/item/view/1913 Summarizing and Visualizing Uncertainty in Non-Rigid Registration.] MICCAI 2010
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</div>

Latest revision as of 21:25, 19 January 2011

Home < 2011 Winter Project Week:UncertaintyVisualization

  • Composite image where the marginal distributions of deformed fibertracts and fMRI activated regions are visualized..

  • The marginal distributions of a deformed fMRI activated region is visualized.

Visualizing Registration Uncertainty

Key Investigators

  • SPL: Petter Risholm, William Wells

Objective

We have developed a probabilistic non-rigid registration framework where we characterize the posterior distribution over deformations with a MCMC method. In practice, a large number of deformations are drawn from the posterior distribution. From the set of deformation samples, we can estimate the most likely deformation as well as the uncertainty of the deformation.

The objective for the Project Week is to developing a command line module that can take the large set of deformation samples and generate useful marginal summarizes/visualizations of the registration uncertainty, e.g. marginal distribution of a biopsy point after registration.

Approach, Plan

Our approach for summarizing and visualizing registration uncertainty is described in [Risholm 2010].

Our plan for the project week is to incorporate the generation of marginal probability maps of points and surface models as a command line module in Slicer3.

Progress

We have developed a:

  1. A VTK class for generating marginal probability maps of deformed surface models.
  2. A command line module for running the code through Slicer3.

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a

  1. ITK Module
  2. Slicer Module
    1. Built-in
    2. Extension -- commandline Yes
    3. Extension -- loadable

References

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