Difference between revisions of "2014 Summer Project Week:Tumor DCE-MRI Segmentation"

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<h3>Objective</h3>
 
<h3>Objective</h3>
 
*Add support for DCE-MRI data-sets with more than 4 post-contrast time-points and for 4D Multi-Volume images.
 
*Add support for DCE-MRI data-sets with more than 4 post-contrast time-points and for 4D Multi-Volume images.
*Implement more sophisticated algorithms for noise reduction, precision, and intra-tumor heterogeneity.
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*Implement more sophisticated algorithms for noise reduction
 
*Add features for intuitive analysis of segmented tumor.
 
*Add features for intuitive analysis of segmented tumor.
  
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
 
*Multiple node input through a dialog box.
 
*Multiple node input through a dialog box.
*Use established SegmentCAD module framework to test new algorithms using numpy.  
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*Use established SegmentCAD module framework to test algorithms to optimize segmentation.  
 
*Add GUI features to manually adjust settings and quickly visualize different segmentation outputs.
 
*Add GUI features to manually adjust settings and quickly visualize different segmentation outputs.
  
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*Segmentation label map further segments tumor-class voxels based on slope ranges of the wash-out curve (persistent, plateau, or washout).
 
*Segmentation label map further segments tumor-class voxels based on slope ranges of the wash-out curve (persistent, plateau, or washout).
 
*Interactive graphing of voxel intensity over time at cursor location, and 3D volume rendering of segmentation label map.
 
*Interactive graphing of voxel intensity over time at cursor location, and 3D volume rendering of segmentation label map.
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<h3>References</h3>
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*https://www.slicer.org/wiki/Documentation/Nightly/Extensions/OpenCAD
  
 
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Latest revision as of 18:01, 10 July 2017

Home < 2014 Summer Project Week:Tumor DCE-MRI Segmentation

Key Investigators

  • Vivek Narayan
  • Jayender Jagadeesan

Project Description

SegmentCAD module for Slicer (openCAD Extension): Segmentation of Breast Tumors from DCE-MRI images.

Objective

  • Add support for DCE-MRI data-sets with more than 4 post-contrast time-points and for 4D Multi-Volume images.
  • Implement more sophisticated algorithms for noise reduction
  • Add features for intuitive analysis of segmented tumor.

Approach, Plan

  • Multiple node input through a dialog box.
  • Use established SegmentCAD module framework to test algorithms to optimize segmentation.
  • Add GUI features to manually adjust settings and quickly visualize different segmentation outputs.

Progress

  • Segmentation for DCE-MRI data-sets with 1 pre-contrast image and 4 post-contrast time-points.
  • Segmentation label map further segments tumor-class voxels based on slope ranges of the wash-out curve (persistent, plateau, or washout).
  • Interactive graphing of voxel intensity over time at cursor location, and 3D volume rendering of segmentation label map.