Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB006733 )
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Transcript of Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB006733 )
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Dinggang Shen
Development and Dissemination of Robust Brain MRI Measurement Tools
(1R01EB006733)
Department of Radiology and BRICUNC-Chapel Hill
IDEA
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Team
• UNC-Chapel Hill - Dinggang Shen - Guorong Wu (postdoc) - Minjeong Kim (postdoc)
• GE - Jim Miller - Xiaodong Tao
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Goal of this project
• To further develop HAMMER registration and white matter lesion (WML) segmentation algorithms, for improving their robustness and performance.
• To design separate software modules for these two algorithms and incorporate them into the 3D Slicer.
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Progress of HAMMER in 2009
• Successfully implemented HAMMER in ITK. (Over 2,000 lines of code)• Integrated HAMMER into Slicer3• Verified and tested its performance in Slicer3
Input Subject
AC/PC
Skull Stripin
g
Segmentation
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Progress of HAMMER in 2009
Template
Subject
Registration result
Typical Registration Result of HAMMER in Slicer3
Average of 18 aligned images
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RABBIT: To speed up our HAMMER registration algorithm (1.5 hours)
12~15 minutes
Subject
e1
e2
(1.5 hours)
Template
Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4):1277-87, Oct 1 2009.
Progress of HAMMER in 2009
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Construct a statistical deformation model
Estimate an intermediate deformation/template
Refine the intermediate deformation field
e1
e2
12~15 minsSubject
Progress of HAMMER in 2009
Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4):1277-87, Oct 1 2009.
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Progress of HAMMER in 2009
Wu et. al., TPS-HAMMER: Improving HAMMER Registration Algorithm by Soft Correspondence Matching and Thin-Plate Splines Based Deformation Interpolation. Neuroimage, 49(3):2225-2233, Feb 2010.
TPS-HAMMER:• Use soft correspondence
detection to robustly establish correspondences for the driving voxels
• Use Thin Plate Splines (TPS) to effectively interpolate deformation fields, based on those estimated at the driving voxels
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Work Plan of HAMMER in 2010
• Further improve HAMMER in Slicer3– Implement RABBIT to speedup the registration– Implement TPS-HAMMER in ITK– Implement intensity-HAMMER in ITK
• Serve HAMMER user community– To provide training and tutorial– To provide technical support– To develop user-friendly interface to the end user
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WML Segmentation• Attribute vector for each point v
},,,{,| 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.
• 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, March 2008.
Neighborhood Ω (5x5x5mm)T1T2PDFLAIR
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Co-registration
Skull-stripping
Intensity normalization
Pre-processing
Manual Segmentation
Training SVM model via training sample and Adaboost
Training
Voxel-wise evaluation & segmentation
Testing
False positive elimination
Post-processing
Progress in 2009• We have implemented all WML segmentation components
in ITK
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Progress in 2009• Have incorporated it into Slicer3
Developer Tools >> White Matter Lesion Segmentation
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Progress in 2009
Training Segmentation
• User interface of WML segmentation in Slicer3
• Input: T1, T2, PD, FLAIR images and lesion ROI of n training subjects• Output: SVM model
• Input: T1, T2, PD, FLAIR images of test subject(s) and trained SVM model• Output: segmented lesion ROI
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• A typical segmentation result
Progress in 2009
Our result Ground truthFLAIR
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• Further development of WML segmentation algorithm– Improve the robustness of multi-modality image registration
(for T1/T2/PD/FLAIR) by using a novel quantitative and qualitative measurement for mutual information
– Design region-adaptive classifiers, in order to allow each classifier for capturing relative simple WML intensity pattern in each region
– Develop a WML atlas for guiding the WML segmentation
• Upgrade of WML lesion segmentation module in Slicer3
Plan of 2010
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Conclusion
Further develop HAMMER registration and WML segmentation algorithms improve their
robustness and performance
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Thank you!
http://bric.unc.edu/IDEAgroup/http://www.med.unc.edu/~dgshen/ IDE
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