Difference between revisions of "2017 Winter Project Week/Diffusely abnormal white matter segmentation with 3d U-net"

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Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
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==Key Investigators==
 
==Key Investigators==
 
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* Sandy Wells (BWH)
 
* Sandy Wells (BWH)
 
* Tina Kapur (BWH)
 
* Tina Kapur (BWH)
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* Sandy Wells (BWH)
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* Charles Guttmann (BWH)
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* Hans Meine (Univ. Bremen, Fh MEVIS)
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==Project Description==
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{| class="wikitable"
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! style="text-align: left; width:27%" |  Objective
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! style="text-align: left; width:27%" |  Approach and Plan
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! style="text-align: left; width:27%" |  Progress and Next Steps
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|- style="vertical-align:top;"
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|
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<!-- Objective bullet points -->
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*  Diffusely abnormal white matter (“DAWM”) are fuzzy-bordered areas of subtly increased signal
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intensity on proton density or T2-weighted images. These abnormalities have been referred to as dirty white matter or dirty-appearing white matter. The goal is to develop an automated system that accurately segments diffusely abnormal white matter.
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|
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<!-- Approach and Plan bullet points -->
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* We would like to use the 3D unet that is shown to be a great architecture for biomedical image segmentation.
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<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
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* The 3D-Unet architecture is implemented in Lasagne and it is currently being trained.
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|}

Latest revision as of 15:29, 6 June 2017

Home < 2017 Winter Project Week < Diffusely abnormal white matter segmentation with 3d U-net

Key Investigators

  • Mohsen Ghafoorian (BWH, Radboud University)
  • Bram Platel (Radboud University)
  • Sandy Wells (BWH)
  • Tina Kapur (BWH)
  • Charles Guttmann (BWH)
  • Hans Meine (Univ. Bremen, Fh MEVIS)

Project Description

Objective Approach and Plan Progress and Next Steps
  • Diffusely abnormal white matter (“DAWM”) are fuzzy-bordered areas of subtly increased signal

intensity on proton density or T2-weighted images. These abnormalities have been referred to as dirty white matter or dirty-appearing white matter. The goal is to develop an automated system that accurately segments diffusely abnormal white matter.

  • We would like to use the 3D unet that is shown to be a great architecture for biomedical image segmentation.
  • The 3D-Unet architecture is implemented in Lasagne and it is currently being trained.
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