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

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__NOTOC__
 
__NOTOC__
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<gallery>
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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:PW2009-v3.png|[[2009_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:Knee.jpg| Knee MRI Image
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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:LabelMap Femur.jpg|Filled Femur Label Map
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Image:Femur Patella Tibia.jpg|EM Segmented Output
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Image:Logo_simbios.gif
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Image:64 58 Affine 350 Iterations.jpeg | 64 Affine Registered on 58
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Image:64 58 PipeLinedAffine Registration 200 Iterations.JPG | 64 Pipeline Affine Registered on 58
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Image:64 58 BSpline 210 Iterations.JPG | 64 Bspline Registered on 58
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Image:42 Masked.jpg | Masked 42 MRI
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Image:64 Masked.jpg | Masked 64 MRI
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Image:42 BSpline To 64 DifferenceAfter.jpg | Registered Image Difference of 42 and 64
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Image:42 Registered To 64.jpg | BSpline Registered Output of 42 and 64
 
</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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* Luis Ibanez, Kitware, Inc.
  
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<div style="margin: 20px;">
 
 
<div style="width: 27%; float: left; padding-right: 3%;">
 
<div style="width: 27%; float: left; padding-right: 3%;">
  
 
<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.
 +
 
 +
Specific Aims
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* Explore the segmentation techniques for knee MRI datasets with special focus on patella, femur and tibia bones.
  
 
</div>
 
</div>
  
−
<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 are working on understanding the capabilities of RegisterImage module in Slicer to apply image-to-image registration on knee datasets.  We are implementing masking algorithms to isolate specific knee bones, and perform parameter exploration to evaluate the sensitivity of registered images to input parameters.  We are exploring the feasibility of applying python ICP-based registration algorithms to directly morph a surface model to a target image geometry.
 +
 
 +
Our goals for the project week are:
 +
* Implement an algorithm to acquire masked regions of interest from MR datasets.
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* Perform and evaluate results from a parameter space exploration study of RegisterImages Batchmake module on knee dataset.
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* Resolve issues in building Python modules from slicer source code.
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* Explore possibility of model-to-image registration using existing atlas (.vtk, .stl) to a target image using Python ICP Registration module.
  
−
Our plan for the project week is to first try out <bar>,...
 
 
</div>
 
</div>
  
−
<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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* Investigated the masking algorithm and completed the module to mask and register images. Need to explore the parameters technique for this code.
 +
* Resolved issues in building Python
 +
* Cluster set up completed
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* Converted Model to masked label map volume to further a masked label volume. Now we have the problem of registering label map volume to an MRI image.
 +
 
  
 
</div>
 
</div>
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</div>
 
−
 
−
 
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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 .
 

Latest revision as of 19:54, 11 April 2023

Home < 2009 Summer Project Week Project Segmentation of Muscoskeletal Images

  • Knee MRI Image

  • Filled Femur Label Map

  • EM Segmented Output

  • Logo simbios.gif

  • 64 Affine Registered on 58

  • 64 Pipeline Affine Registered on 58

  • 64 Bspline Registered on 58

  • Masked 42 MRI

  • Masked 64 MRI

  • Registered Image Difference of 42 and 64

  • BSpline Registered Output of 42 and 64


Key Investigators

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

Specific Aims

  • Explore the segmentation techniques for knee MRI datasets with special focus on patella, femur and tibia bones.

Approach, Plan

We are working on understanding the capabilities of RegisterImage module in Slicer to apply image-to-image registration on knee datasets. We are implementing masking algorithms to isolate specific knee bones, and perform parameter exploration to evaluate the sensitivity of registered images to input parameters. We are exploring the feasibility of applying python ICP-based registration algorithms to directly morph a surface model to a target image geometry.

Our goals for the project week are:

  • Implement an algorithm to acquire masked regions of interest from MR datasets.
  • Perform and evaluate results from a parameter space exploration study of RegisterImages Batchmake module on knee dataset.
  • Resolve issues in building Python modules from slicer source code.
  • Explore possibility of model-to-image registration using existing atlas (.vtk, .stl) to a target image using Python ICP Registration module.

Progress

  • Investigated the masking algorithm and completed the module to mask and register images. Need to explore the parameters technique for this code.
  • Resolved issues in building Python
  • Cluster set up completed
  • Converted Model to masked label map volume to further a masked label volume. Now we have the problem of registering label map volume to an MRI image.


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