Difference between revisions of "2017 Winter Project Week/MeningiomaSegmentation"

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* Try Slicer in a Nipype workflow.
 
* Try Slicer in a Nipype workflow.
 
* Apply manifold learning (a method of dimensionality reduction).
 
* Apply manifold learning (a method of dimensionality reduction).
 +
* Get more data, and potentially train a neural network.
 
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Revision as of 01:25, 13 January 2017

Home < 2017 Winter Project Week < MeningiomaSegmentation

Key Investigators

  • Satrajit Ghosh, MIT
  • Omar Arnaout, Brigham and Women's Hospital

Project Description

Objective Approach and Plan Progress and Next Steps
  • Segment meningiomas in structural MR images.
  • Assess state of brain after surgical removal of meningioma.
  • Evaluate performance of existing segmentation methods.

Progress

  • Segmented with ANTs and FSL.
  • Learned about Slicer segmentation tools, and segmented semi-automatically.
  • Put in contact with people who have segmented meningiomas.

Next steps

  • Continue learning about what has been done in the past.
  • Improve brain-extraction.
  • Continue testing existing segmentation methods.
  • Try Slicer in a Nipype workflow.
  • Apply manifold learning (a method of dimensionality reduction).
  • Get more data, and potentially train a neural network.

Background and References

MR images of meningiomas that will be used in this project are available at OpenNeu.ro.

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