Difference between revisions of "2010 Winter Project Week Musco Skeletal Segmentation"

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Image:IdealSpgrWater.jpg‎|Ideal Spgr Water MR image
 
Image:IdealSpgrWater.jpg‎|Ideal Spgr Water MR image
 
Image:SegmentationOutput.jpg | Segmentation Output
 
Image:SegmentationOutput.jpg | Segmentation Output
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Image:ScatterPlot.jpg | Scatter Plot
 
 
Image:Bones.jpg | Bones
 
Image:Bones.jpg | Bones
 
Image:cartilage.jpg | Cartilage
 
Image:cartilage.jpg | Cartilage
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Image:SlicerOutput.jpg | Slicer Output
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Image:IdealSpgrWaterHip.jpg | SpgrWater_Hip
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Image:IdealSpgrOpHip.jpg | SpgrFat_Hip
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Image:HipOut1.jpg | Hip Output
 
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==Key Investigators==
 
==Key Investigators==
 
* Stanford: Harish Doddi, Saikat Pal, Scott Delp
 
* Stanford: Harish Doddi, Saikat Pal, Scott Delp
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* Kitware: Luis Ibanez
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* Kitware: Luis Ibanez, Harvey Cline
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* GE Research: Xiaodong Tao
  
 
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<div style="margin: 20px;">
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<h3>Objective</h3>
 
<h3>Objective</h3>
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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.
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The aim of this project is to develop a methodology for rapid segmentation of knee structures from magnetic resonance (MR) images for subject-specific modeling. The overall goal can be broken down into two specific objectives -
 +
<BR>
 +
1. Rapid segmentation of target structures into label maps.
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<BR>
 +
2. Generation of simulation-ready models from existing atlas and label maps of individual structures.
 
</div>
 
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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Our earlier work focused on segmenting the left atrium (LA), the heart chamber on which RF ablations are usually done, in blood pool MR images. We used a label fusion segmentation algorithm which first registered all of the training images to the test one and then employed a weighted voting procedure at each voxel.
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<i><u>Approach:</u></i>
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<BR>
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Objective 1: We have adopted a multi-contrast MR methodology to segment knee bones and cartilage structures.  The algorithm utilizes tissue intensity information from multiple MR contrasts to segment structures of interest.  Inputs to the algorithm included n registered MR image sets. The algorithm created an n-dimensional space of voxel intensities associated with the n image sets. The user assigned seed points to the structures of interest, and the algorithm created a cluster center for each structure of interest. Cluster radii were calculated based on standard deviations obtained from seed points, and tissue structures were classified as label maps.  
  
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Given corresponding cardiac blood pool and post-procedure delayed enhancement images for each patient, our plan is to first segment the LA in the blood pool image, then transfer this segmentation to the delayed enhancement image of the same patient. We intend to use this prior information while searching for the ablation scar using intensity based algorithms. This prior knowledge of the LA location will allow us to avoid most false positives.
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For more details, please visit http://www.na-mic.org/Wiki/index.php/Stanford_Simbios_group
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<BR>
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<i><u>Plan: </u></i>
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<BR>
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a. Incorporate a method to refine label maps (remove undesired bridge connections). 
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<BR>
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b. Implement an algorithm to assign fiducial points to region boundaries for manual adjustments of geometries.  
  
 
</div>
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
−
We have only done some very preliminary ablation scar segmentation experiments.
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* Implemented algorithm to segment structures of interest from multi-constrast MR images.
 +
* Generated label maps of knee and hip structures. 
 +
* Implemented a pipeline to rapidly refine label maps to isolate regions of interest. 
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* Identified an approach to trace geometry boundary and automatically assign fiducial points.
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</div>
 
</div>
 
</div>
 
</div>

Latest revision as of 16:28, 7 January 2010

Home < 2010 Winter Project Week Musco Skeletal Segmentation

  • Ideal Spgr Fat MR image

  • Ideal Spgr Water MR image

  • Segmentation Output

  • Bones

  • Cartilage

  • SpgrWater_Hip

  • SpgrFat_Hip

  • Hip Output


Key Investigators

  • Stanford: Harish Doddi, Saikat Pal, Scott Delp
  • Kitware: Luis Ibanez, Harvey Cline
  • GE Research: Xiaodong Tao

Objective

The aim of this project is to develop a methodology for rapid segmentation of knee structures from magnetic resonance (MR) images for subject-specific modeling. The overall goal can be broken down into two specific objectives -
1. Rapid segmentation of target structures into label maps.
2. Generation of simulation-ready models from existing atlas and label maps of individual structures.

Approach, Plan

Approach:
Objective 1: We have adopted a multi-contrast MR methodology to segment knee bones and cartilage structures. The algorithm utilizes tissue intensity information from multiple MR contrasts to segment structures of interest. Inputs to the algorithm included n registered MR image sets. The algorithm created an n-dimensional space of voxel intensities associated with the n image sets. The user assigned seed points to the structures of interest, and the algorithm created a cluster center for each structure of interest. Cluster radii were calculated based on standard deviations obtained from seed points, and tissue structures were classified as label maps.

For more details, please visit http://www.na-mic.org/Wiki/index.php/Stanford_Simbios_group
Plan:
a. Incorporate a method to refine label maps (remove undesired bridge connections).
b. Implement an algorithm to assign fiducial points to region boundaries for manual adjustments of geometries.

Progress

  • Implemented algorithm to segment structures of interest from multi-constrast MR images.
  • Generated label maps of knee and hip structures.
  • Implemented a pipeline to rapidly refine label maps to isolate regions of interest.
  • Identified an approach to trace geometry boundary and automatically assign fiducial points.


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