Difference between revisions of "2011 Winter Project Week:TubeTK VascularImageSegmentationAndAnalysis"

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* Kitware: Stephen Aylward, Danielle Pace
 
* Kitware: Stephen Aylward, Danielle Pace
 
* SPL: Steve Pieper
 
* SPL: Steve Pieper
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* Luca Antiga, Daniel Haehn
  
 
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<h3>Objective</h3>
 
<h3>Objective</h3>
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[http://public.kitware.com/Wiki/TubeTK TubeTK] is a new open-source toolkit that hosts algorithms for applications involving images of tubes. By focusing on the geometry of tubes we can accomplish many of the grand challenges in medical image analysis for a wide range significant cases, e.g., disease detection, diagnosis, treatment guidance, and monitoring using vascular features.
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[http://public.kitware.com/Wiki/TubeTK TubeTK] is a new open-source toolkit that hosts algorithms for applications involving images of tubes.
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Two driving applications:
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* Surgical guidance: registering pre-operative vascular models with intra-operative images (e.g., ultrasound)
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* Characterizing vascular patters: using graph theory to distinguish clinical populations based on vascular patterns (e.g., benign -vs- malignant tumors via tortuosity)
  
 
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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TODO
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* Python module in Slicer 4 for centerline and radius estimation of vasculature in brain MRA
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** Workflow: brain envelop segmentation, seeding, extraction
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* Integration with VMTK
  
 
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Revision as of 16:48, 6 January 2011

Home < 2011 Winter Project Week:TubeTK VascularImageSegmentationAndAnalysis

Key Investigators

  • Kitware: Stephen Aylward, Danielle Pace
  • SPL: Steve Pieper
  • Luca Antiga, Daniel Haehn

Objective

TubeTK is a new open-source toolkit that hosts algorithms for applications involving images of tubes.

Two driving applications:

  • Surgical guidance: registering pre-operative vascular models with intra-operative images (e.g., ultrasound)
  • Characterizing vascular patters: using graph theory to distinguish clinical populations based on vascular patterns (e.g., benign -vs- malignant tumors via tortuosity)

Approach, Plan

  • Python module in Slicer 4 for centerline and radius estimation of vasculature in brain MRA
    • Workflow: brain envelop segmentation, seeding, extraction
  • Integration with VMTK

Progress

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a:

  1. ITK Module
  2. Slicer Module
    1. Built-in
    2. Extension -- commandline YES
    3. Extension -- loadable
  3. Other YES

All software written during the project week will be contributed to TubeTK, and algorithms will be incorporated into 3D Slicer as CLI applications.

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