Difference between revisions of "2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation"

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__NOTOC__
 
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<gallery>
 
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
 
Image:PW-MIT2012.png|[[2012_Summer_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:MRI1.jpg|Example of manual segmentation of the left atrial wall.  
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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:Pic2.jpg|Polar transform of the left atrial wall.
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Image:Pic3.jpg|Initial segmentation results.  
 
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==Instructions for Use of this Template==
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==Background==
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#Please create a new wiki page with an appropriate title for your project using the convention 2012_Winter_Project_Week:<Project Name>
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Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not.
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#Copy the entire text of this page into the page created above
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Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically.
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#Link the created page into the list of projects for the project event
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Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function.
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#Delete this section from the created page
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These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical.
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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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* BU: Arie Nakhmani
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* Utah: Tom Fletcher, Ross Whitaker
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* BU and CCC UAB: Allen Tannenbaum
  
 
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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 are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.
  
  
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
  
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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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* Implementing the comparison framework
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* Ellipsoidal MRI transform
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* Local ridge extraction
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* Topological persistence algorithm implementation
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* Comparison of different topological simplification approaches
  
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Our plan for the project week is to first try out <bar>,...
 
  
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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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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We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.  
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This week:
 +
* We have developed general error computation framework for segmentation features quantitative comparison.
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* Two topological simplification algorithms were implemented and compared to a regular edge base segmentation. Both implemented algorithms show about 40% reduction in error.
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* In the future, we plan to compare more topological simplifications to improve the error.
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</div>
 
</div>
 
</div>
 
</div>
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==Delivery Mechanism==
 
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This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)
 
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#ITK Module
 
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#Slicer Module
 
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##Built-in
 
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##Extension -- commandline
 
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##Extension -- loadable
 
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#Other (Please specify)
 
  
 
==References==
 
==References==
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*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
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# Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5
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* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
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# Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002
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* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
 
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* Goodlett C, Corouge I, Jomier M, Gerig G, 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 13:01, 22 June 2012

Home < 2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation

  • Example of manual segmentation of the left atrial wall.

  • Polar transform of the left atrial wall.

  • Initial segmentation results.

Background

Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not. Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function. These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical.

Key Investigators

  • BU: Arie Nakhmani
  • BU and CCC UAB: Allen Tannenbaum

Objective

We are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.




Approach, Plan

  • Implementing the comparison framework
  • Ellipsoidal MRI transform
  • Local ridge extraction
  • Topological persistence algorithm implementation
  • Comparison of different topological simplification approaches


Progress

We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.

This week:

  • We have developed general error computation framework for segmentation features quantitative comparison.
  • Two topological simplification algorithms were implemented and compared to a regular edge base segmentation. Both implemented algorithms show about 40% reduction in error.
  • In the future, we plan to compare more topological simplifications to improve the error.


References

  1. Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5
  2. Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002
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