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

From NAMIC Wiki
Jump to: navigation, search
 
(6 intermediate revisions by 3 users not shown)
Line 1: Line 1:
 +
__NOTOC__
 +
<gallery>
 +
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
 +
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" -->
 +
</gallery>
 +
 
==Key Investigators==
 
==Key Investigators==
 
<!-- Add a bulleted list of investigators and their institutions here -->
 
<!-- Add a bulleted list of investigators and their institutions here -->
Line 5: Line 11:
 
* Sandy Wells (BWH)
 
* Sandy Wells (BWH)
 
* Tina Kapur (BWH)
 
* Tina Kapur (BWH)
−
* Sandy Wells (BWH)
+
* Charles Guttmann (BWH)
−
 
+
* Hans Meine (Univ. Bremen, Fh MEVIS)
  
 
==Project Description==
 
==Project Description==
Line 16: Line 22:
 
|
 
|
 
<!-- Objective bullet points -->
 
<!-- Objective bullet points -->
−
* Develop the client side as a Slicer extension.
+
* Diffusely abnormal white matter (“DAWM”) are fuzzy-bordered areas of subtly increased signal
−
* Develop the server side.
+
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.
−
* Train a diabetic retinopathy classifier and add the model to the DeepInfer model repository.
 
 
|
 
|
 
<!-- Approach and Plan bullet points -->
 
<!-- Approach and Plan bullet points -->
−
*
+
* We would like to use the 3D unet that is shown to be a great architecture for biomedical image segmentation.
 
|
 
|
 
<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
 
<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
−
*
+
* The 3D-Unet architecture is implemented in Lasagne and it is currently being trained.
 
|}
 
|}

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.
Retrieved from "https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/Diffusely_abnormal_white_matter_segmentation_with_3d_U-net&oldid=96113"