Difference between revisions of "2009 Summer Project Week Project Segmentation of Muscoskeletal Images"

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<gallery widths="400px" perrow="6">
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Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
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Image:Knee.jpg| Knee MRI Image
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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:All Three.JPG|Pre-Segmented Femur/Patella/Tibia Model.
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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:Femur Patella Tibia.jpg|EM Segmented Output.
 
</gallery>
 
</gallery>
  
  
 
==Key Investigators==
 
==Key Investigators==
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* Stanford: Harish Doddi, Saikat Pal, Scott Delp
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* Utah: Tom Fletcher, Ross Whitaker
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* Harvard: Ron Kikinis
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* Steve Pieper, Isomics, Inc.
  
 
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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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The aim of this project is to develop an automatic/semi-automatic methodology to convert whole body imaging datasets into three-dimensional models for neuromuscular biomechanics and finite element simulations.  
  
 
</div>
 
</div>
  
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<div style="width: 27%; float: left; padding-right: 3%;">
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<div style="width: 30%; float: left; padding-right: 3%;">
  
 
<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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We focus on developing library of atlases for specific joints (eg: the knee) and evaluating EM Segmenter Algorithms. The main challenge during this process is the study of techniques for registration of a patient MRI image to a given patient MRI image (for which we already have atlas). Our approach to achieve this is to conduct parameter exploration study using the module '''Register Images Batchmake''' in Slicer 3.4
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We are also investigating another technique where we try to register a given knee model to a patient MRI Image using '''Python ICP Registration'''.
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Our plan for the project week is to
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  '''''a'''''. Perform and evaluate results from an extensive
 +
  parameter space exploration study of
 +
  RegisterImages Batchmake module on knee dataset.
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  '''''b'''''. Resolve issues in building Python modules from
 +
  slicer source code.
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  '''''c'''''. Demonstrate proof of concept on registering an
 +
  existing atlas (.vtk, .stl) to a target image
 +
  using Python ICP Registration module.
 +
  
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Our plan for the project week is to first try out <bar>,...
 
 
</div>
 
</div>
  
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<div style="width: 40%; float: left;">
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<div style="width: 27%; float: left;">
  
 
<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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</div>
 
</div>
 
</div>
 
</div>
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−
 
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==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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* 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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* 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 .
 

Revision as of 01:00, 29 May 2009

Home < 2009 Summer Project Week Project Segmentation of Muscoskeletal Images

  • Knee MRI Image

  • Pre-Segmented Femur/Patella/Tibia Model.

  • EM Segmented Output.


Key Investigators

  • Stanford: Harish Doddi, Saikat Pal, Scott Delp
  • Harvard: Ron Kikinis
  • Steve Pieper, Isomics, Inc.

Objective

The aim of this project is to develop an automatic/semi-automatic methodology to convert whole body imaging datasets into three-dimensional models for neuromuscular biomechanics and finite element simulations.

Approach, Plan

We focus on developing library of atlases for specific joints (eg: the knee) and evaluating EM Segmenter Algorithms. The main challenge during this process is the study of techniques for registration of a patient MRI image to a given patient MRI image (for which we already have atlas). Our approach to achieve this is to conduct parameter exploration study using the module Register Images Batchmake in Slicer 3.4

We are also investigating another technique where we try to register a given knee model to a patient MRI Image using Python ICP Registration.

Our plan for the project week is to 

 a. Perform and evaluate results from an extensive 
 parameter space exploration study of 
 RegisterImages Batchmake module on knee dataset.
 b. Resolve issues in building Python modules from 
 slicer source code.
 c. Demonstrate proof of concept on registering an 
 existing atlas (.vtk, .stl) to a target image 
 using Python ICP Registration module.

Progress


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