Difference between revisions of "ProjectWeek200706:vtkITKWrapperForRuleBasedSegmentation"

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|[[Image:ProjectWeek-2007.png|thumb|320px|Return to [[2007_Programming/Project_Week_MIT|Project Week Main Page]] ]]
 
|[[Image:ProjectWeek-2007.png|thumb|320px|Return to [[2007_Programming/Project_Week_MIT|Project Week Main Page]] ]]
|[[Image:genuFAp.jpg|thumb|320px|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.]]
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|[[Image:Dlpfc-slicer.png|thumb|120px|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.]]
|[[Image:genuFA.jpg|thumb|320px|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.]]
 
 
|}
 
|}
  
 
__NOTOC__
 
__NOTOC__
 
===Key Investigators===
 
===Key Investigators===
*Georgia Tech: John Melonakos
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* Georgia Tech: John Melonakos, Ramsey Al-Hakim
 
* Kitware: Brad Davis
 
* Kitware: Brad Davis
 
* BWH: Marek Kubicki
 
* BWH: Marek Kubicki
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* UCI: Jim Fallon
  
 
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<div style="width: 27%; float: left; padding-right: 3%;">
 
<div style="width: 27%; float: left; padding-right: 3%;">
 
<h1>Objective</h1>
 
<h1>Objective</h1>
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 are developing rule-based segmentation techniques which speedup the process and improve the accuracy for delineating the DLPFC in brain MRI scans. Our objective is to develop Slicer modules to facilitate clinical use of these techniques.
 
 
  
 +
A functional Slicer2 module has been developed and now needs to be tested and used by our Core 3 partners.  Furthermore, in order to provide continued support for this module, we will port this code to Slicer3.
 
</div>
 
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<h1>Approaches and Challenges </h1>
 
<h1>Approaches and Challenges </h1>
 
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Our approach for segmenting the DLPFC is described in the references below.  The challenge is to make this software user-friendly to enable clinical use of the tool.
Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below.  The main challenge to this approach is <foo>.
 
 
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This is where you put in progress made in Project Week 2007.
 
This is where you put in progress made in Project Week 2007.
  
====January 2007 Project Half Week====
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====2005-2007====
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.
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This code was developed between 2005-2007.  First is was developed and tested in Matlab. Then the sub-volume creation rules were ported to Slicer2 while the Bayesian segmentation was ported to ITK (see the references below for more detail). Finally, in early 2007, a vtk wrapper of the ITK Bayesian code was developed, thus completing the Slicer2 RuleBasedSegmentation module.
  
 
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===Publications===
 
===Publications===
* Fletcher, P.T., Tao, R., Jeong, W.-K., Whitaker, R.T., "A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI," to appear Information Processing in Medical Imaging (IPMI) 2007.
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* Ramsey Al-Hakim, James Fallon, Delphine Nain, John Melonakos, and Allen Tannenbaum. A dorsolateral prefrontal cortex semi-automatic segmenter. In SPIE Medical Imaging, 2006.
* Corouge, I., Fletcher, P.T., Joshi, S., Gilmore, J.H., and Gerig, G., "Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis," Medical Image Analysis 10 (2006), 786--798.
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* J. Melonakos, K. Krishnan, and A. Tannenbaum. An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter. Insight Journal, 2006.
* Corouge, I., Fletcher, P.T., Joshi, S., Gilmore J.H., and Gerig, G., Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis, Lecture Notes in Computer Science LNCS, James S. Duncan and Guido Gerig, editors, Springer Verlag, Vol. 3749, Oct. 2005, pp. 131 -- 138
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* J. Melonakos, R. Al-Hakim, J. Fallon, and A. Tannenbaum. Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit. Insight Journal, 2005.
* C. Goodlett, I. Corouge, M. Jomier, and G. Gerig, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .
 

Revision as of 02:07, 25 May 2007

Home < ProjectWeek200706:vtkITKWrapperForRuleBasedSegmentation
Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.


Key Investigators

  • Georgia Tech: John Melonakos, Ramsey Al-Hakim
  • Kitware: Brad Davis
  • BWH: Marek Kubicki
  • UCI: Jim Fallon

Objective

We are developing rule-based segmentation techniques which speedup the process and improve the accuracy for delineating the DLPFC in brain MRI scans. Our objective is to develop Slicer modules to facilitate clinical use of these techniques.

A functional Slicer2 module has been developed and now needs to be tested and used by our Core 3 partners. Furthermore, in order to provide continued support for this module, we will port this code to Slicer3.

Approaches and Challenges

Our approach for segmenting the DLPFC is described in the references below. The challenge is to make this software user-friendly to enable clinical use of the tool.

Progress


June 2007 Project Week

This is where you put in progress made in Project Week 2007.

2005-2007

This code was developed between 2005-2007. First is was developed and tested in Matlab. Then the sub-volume creation rules were ported to Slicer2 while the Bayesian segmentation was ported to ITK (see the references below for more detail). Finally, in early 2007, a vtk wrapper of the ITK Bayesian code was developed, thus completing the Slicer2 RuleBasedSegmentation module.



Publications

  • Ramsey Al-Hakim, James Fallon, Delphine Nain, John Melonakos, and Allen Tannenbaum. A dorsolateral prefrontal cortex semi-automatic segmenter. In SPIE Medical Imaging, 2006.
  • J. Melonakos, K. Krishnan, and A. Tannenbaum. An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter. Insight Journal, 2006.
  • J. Melonakos, R. Al-Hakim, J. Fallon, and A. Tannenbaum. Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit. Insight Journal, 2005.