Difference between revisions of "2008 Summer Project Week:LobeParcellation"

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|[[Image:ProjectWeek-2008.png|thumb|320px|Return to [[2008_Summer_Project_Week|Project Week Main Page]] ]]
 
|[[Image:ProjectWeek-2008.png|thumb|320px|Return to [[2008_Summer_Project_Week|Project Week Main Page]] ]]
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|[[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:RoiPar.PNG|thumb|320px|Lobe parcellations in a coronal slice]]
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|[[Image:genuFA.jpg|thumb|320px|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.]]
 
 
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__NOTOC__
 
__NOTOC__
  
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===Instructions for Use of this Template===
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===Key Investigators===
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#Please create a new wiki page with an appropriate title for your project using the convention NA-MIC/Projects/Theme-Name/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
 
  
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===Key Investigators===
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* BWH: Sylvain Bouix, Yogesh Rathi, Padmapriya Srinivasan
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* SPL: Kilian Pohl
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* Utah: Tom Fletcher, Ross Whitaker
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* Kitware: Brad Davis
  
  
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<h1>Objective</h1>
 
<h1>Objective</h1>
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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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To build a lobe parcellation algorithm for our 3T data set. The spatial priors are based on 100 existing manually-lobe parcellated 1.5T data.
  
  
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<h1>Approach, Plan</h1>
 
<h1>Approach, Plan</h1>
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Our plan is to use an algorithm described in Reference 1 to construct probability maps of the lobes using an unbiased registration approach. The algorithm uses a ''label space'' representation that allows for direct registration.
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We will then tune the EMsegmenter of Slicer3 to perform the segmentation.
  
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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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Our plan for the project week is to first try out <bar>,...
 
 
</div>
 
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<h1>Progress</h1>
 
<h1>Progress</h1>
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* We have been using a MATLAB(R) implementation of the atlas building algorithm to construct the atlas and fine tuning it for 10 subjects.  
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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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* Debugging matlab code
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</div>
 
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===References===
  
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===References===
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#James Malcolm, Yogesh Rathi,and Allen Tannenbaum, "Label Space: A multi-object Shape Representation", IWCIA 2008,LNCS 4958, pp. 185-196.
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* 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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#Motoaki Nakamura, Dean F. Salisbury, Yoshio Hirayasu, Sylvain Bouix, Kilian M. Pohl, Takeshi Yoshida, Min-Seong Koo, Martha E. Shenton, and Robert W. McCarley, "Neocortical Gray Matter Volume in First-Episode Schizophrenia and First-Episode Affective Psychosis: A Cross-Sectional and Longitudinal MRI Study", Biol Psychiatry 2007;62:773–783
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* 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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* 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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* 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 .
 

Latest revision as of 14:05, 27 June 2008

Home < 2008 Summer Project Week:LobeParcellation
Return to Project Week Main Page
Lobe parcellations in a coronal slice



Key Investigators

  • BWH: Sylvain Bouix, Yogesh Rathi, Padmapriya Srinivasan
  • SPL: Kilian Pohl
  • Kitware: Brad Davis


Objective

To build a lobe parcellation algorithm for our 3T data set. The spatial priors are based on 100 existing manually-lobe parcellated 1.5T data.


Approach, Plan

Our plan is to use an algorithm described in Reference 1 to construct probability maps of the lobes using an unbiased registration approach. The algorithm uses a label space representation that allows for direct registration. We will then tune the EMsegmenter of Slicer3 to perform the segmentation.

Progress

  • We have been using a MATLAB(R) implementation of the atlas building algorithm to construct the atlas and fine tuning it for 10 subjects.
  • Debugging matlab code


References

  1. James Malcolm, Yogesh Rathi,and Allen Tannenbaum, "Label Space: A multi-object Shape Representation", IWCIA 2008,LNCS 4958, pp. 185-196.
  2. Motoaki Nakamura, Dean F. Salisbury, Yoshio Hirayasu, Sylvain Bouix, Kilian M. Pohl, Takeshi Yoshida, Min-Seong Koo, Martha E. Shenton, and Robert W. McCarley, "Neocortical Gray Matter Volume in First-Episode Schizophrenia and First-Episode Affective Psychosis: A Cross-Sectional and Longitudinal MRI Study", Biol Psychiatry 2007;62:773–783
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