white matter lesion segmentation tutorial
DESCRIPTION
WMLTRANSCRIPT
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NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
White Matter Lesion Segmentation
Minjeong Kim, Dinggang Shen
UNC Chapel Hill
Xiaodong Tao, Jim Miller
GE Research
NA-MIC Tutorial Contest: Summer 2010
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Learning Objective
Learn how to run “White Matter Lesion
Segmentation” module in Slicer 3.
![Page 3: White Matter Lesion Segmentation Tutorial](https://reader033.vdocuments.us/reader033/viewer/2022050809/55cf9a10550346d033a04ff9/html5/thumbnails/3.jpg)
National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Pre-requisite
• Data Loading and Visualization (Sonia
Pujol, Ph.D.)
– http://www.na-
mic.org/Wiki/index.php/Slicer3.2:Training
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Material
• This tutorial requires Slicer3.6 (release
version) and the tutorial dataset. They are
available at the following locations:
• Slicer3.6 download page http://www.slicer.org/pages/Downloads/
• Tutorial dataset: http://wiki.na-
mic.org/Wiki/index.php/File:White_Matter_Lesion_Segmenta
tion_TutorialContestSummer2010.zip
Disclaimer: It is the responsibility of the user of Slicer to comply with both the terms
of the license and with the applicable laws, regulations, and rules.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Input: N training images (T1, T2, PD, FLAIR, lesion ROI)
N
Material: Sample Data
• Training data
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Input: testing image (T1, T2, PD, FLAIR)
Material: Sample Data
• Testing data
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Platform
• This tutorial has tested on a Linux
(64 bit) machine.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Overview
• Introduction
• Getting started
• Pipeline 1 - Training & Segmentation
only
• Pipeline 2 - Preprocessing, Training,
and Segmentation
• Conclusion
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Introduction
• Learning based WML segmentation
Lao, Shen, et al., "Computer-Assisted Segmentation of White Matter Lesions in 3D MR
images Using Support Vector Machine", Academic Radiology, 15(3):300-313, 2008.
},,,{,| 21 FLAIRPDTTmvttIvF mmm
• SVM To train a WML segmentation classifier.
• Adaboost To adaptively weight the training samples and
improve the generalization of WML segmentation method.
Neighborhood Ω (5x5x5mm) T1
T2
PD FLAIR
Lesion voxel
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Overview
• Introduction
• Getting started
• Pipeline 1 - Training & Segmentation
only
• Pipeline 2 - Preprocessing, Training,
and Segmentation
• Conclusion
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Getting started
• Module installation
• Press F2 or go to View >> Application
Settings >> Module Settings on the
menu of Slicer3.
• Click the “add a preset” button.
• Select the location of the White Matter
Lesion Segmentation modules
(wmlstrain and wmlstest).
• Close Slicer3 and restart.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
• Execution
Select “White Matter
Lesion Segmentation
Training” for Training,
“White Matter Lesion
Segmentation” for
Segmentation
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Overview
• Introduction
• Getting started
• Pipeline 1 - Training &
Segmentation only
• Pipeline 2 - Preprocessing, Training,
and Segmentation
• Conclusion
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Pipeline 1 (w/o Preprocessing)
• In case your images are already
preprocessed…
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Training
Select “White
Matter Lesion
Segmentation
Training”
Check image
modalities you
want to train,
e.g. T1,T2,PD,
and FLAIR
DO NOT check
this box to skip
preprocessing
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Click and select the location
containing training images
Click and select the location
where SVM model will be saved
after training
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
303D20268
305D40291
301D00368
303D20146
604H30067
303D20258
303D20153
302D10226
303D20114
Click and select the text file
containing the list of filenames of
training images
Example)
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Click and select the file containing
the list of prefixes of training images
Example) .T1.byte.cbq.match.smooth.hdr
.T2.byte.cbq.match.smooth.hdr
.PD.byte.cbq.match.smooth.hdr
.FL.byte.cbq.match.smooth.hdr
.lesion.mask.hdr
.lesion.mask.open.hdr
.lesion.premask.hdr
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Training
If all parameters are selected, press
“Apply”.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Testing (Segmentation)
Select “White
Matter Lesion
Segmentation
Training”
DO NOT check
this box to skip
preprocessing
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Click and specify the location
containing the saved SVM
models in the “Training” stage.
Click and select the location
containing testing images.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Click and select the file containing
the list of filename of testing image.
Example: 601H03166
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Click and select the file
containing the list of prefixes of
testing image.
Example: .T1.byte.cbq.match.smooth.hdr
.T2.byte.cbq.match.smooth.hdr
.PD.byte.cbq.match.smooth.hdr
.FL.byte.cbq.match.smooth.hdr
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Set the filename to save the
segmented lesion volume in the
end of “Testing” stage.
If all parameters are selected, press
“Apply”.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
• Visualization of the segmented lesion volume
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Overview
• Introduction
• Getting started
• Pipeline 1 - Training & Segmentation
only
• Pipeline 2 - Preprocessing,
Training, and Segmentation
• Conclusion
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Pipeline (w/ Preprocessing)
• If your images are unprocessed…
Co-registration
Skull stripping
Bias correction
Pre-processing
Manual Segmentation
Train SVM model via
training samples and
Adaboost
Training
Voxel-wise evaluation &
segmentation
Testing
Histogram matching
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Training/Testing w/ Preprocessing
• In the training or testing menu, check the
“Preprocessing” option.
• Intermediate files by processing steps are
saved in the directory you specified in the
training/testing menu.
• For other training and testing options, see
page 12-24.
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Check this box
for preprocessing
before “training”. Do same thing for
“testing”.
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National Alliance for Medical Image Computing
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• Example of preprocessing – coregistration (FLAIR)
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
• Example of preprocessing – skull stripping
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
• Example of preprocessing – bias correction and
histogram matching
![Page 33: White Matter Lesion Segmentation Tutorial](https://reader033.vdocuments.us/reader033/viewer/2022050809/55cf9a10550346d033a04ff9/html5/thumbnails/33.jpg)
National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Overview
• Introduction
• Getting started
• Pipeline 1 - Training & Segmentation
only
• Pipeline 2 - Preprocessing, Training,
and Segmentation
• Conclusion
![Page 34: White Matter Lesion Segmentation Tutorial](https://reader033.vdocuments.us/reader033/viewer/2022050809/55cf9a10550346d033a04ff9/html5/thumbnails/34.jpg)
National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Conclusion
• A Slicer3 module for automatic segmentation of
white matter lesions has been developed.
– Preprocessing
• Coregistration, skull stripping, bias correction, and
histogram matching
– Training
• Build SVM model using multi-protocol MRIs (T1,
T2, PD, and FLAIR)
– Segmentation
• Test new subject images using the SVM model
built in the training stage
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National Alliance for Medical Image Computing
http://na-mic.org © 2010, ARR
Acknowledgments
• National Alliance for Medical Image
Computing
NIH U54EB005149
Guorong Wu, Ph.D.
UNC Chapel Hill