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Image Display, Enhancement, and Analysis. IDEA. Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB006733 ). Dinggang Shen. Department of Radiology and BRIC UNC-Chapel Hill. UNC-Chapel Hill - Dinggang Shen - 1/2 Postdoctoral fellow(s) UPenn - PowerPoint PPT Presentation
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Dinggang Shen
Development and Dissemination of
Robust Brain MRI Measurement Tools
(1R01EB006733)
Department of Radiology and BRICUNC-Chapel Hill
IDEA
Team
• UNC-Chapel Hill
- Dinggang Shen
- 1/2 Postdoctoral fellow(s)
• UPenn
- Christos Davatzikos
• GE
- Jim Miller
- Xiaodong Tao
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.
PACS Database Format
Converter
WML Segmentation Algorithm
Visualization Engine
Tissue Classification
HAMMER Complexity
Levels
Parameter Tuning
Learn Best Features
Deformation Constraints
Models
Tissue Density Maps
ROI Labeling
Group Analysis
ROI-based Analysis
HAMMER Registration Algorithm
Skull Stripping
SPM
Data importer
Data processing
Registration
Applications
Training
Skull Stripping
Multimodality Registration
Intensity Normalization
Manual Segmentation
Training SVM Classifier
Voxel-wise Segmentation
False-Positive Elimination
Application
MI
Q-MI
WML Atlas
Data processing
Overview of Our Brain Measurement Tools
• To further develop HAMMER registration and 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.
Matching attribute vectors
Image registration and warping
Shen, et al., “HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration”, IEEE Trans. on Medical Imaging, 21(11):1421-1439, Nov 2002. (2006 Best Paper Award, IEEE Signal Processing Society)
HAMMER
Model:Individual:
(1) Formulated as correspondence detection
Registration – HAMMER
Difficulty: High variations of brain structures
How can we detect correspondences?
Solution: Use both global and local image features to represent anatomical structures, such as using wavelets or geometrical moments.
Xue, Shen, et al., “Determining Correspondence in 3D MR Brain Images Using Attribute Vectors as Morphological Signatures of Voxels”, IEEE Trans. on Medical Imaging, 23(10): 1276-1291, Oct 2004.
Distinctive character of attribute vector:
toward an anatomical signature of every voxel
Examples of attribute vector similarity maps, and point correspondences
Brain A Brain B Similarity Map
(2) Hierarchical registration – reliable points first
HAMMER
To minimize the effect of local minima
Few driving voxels
Smooth approximation of the energy function
Many driving voxels
Complete energy function
Voxels with distinct attribute vectors.Roots of sulci
Crowns of gyriAll boundary voxels
Beginning of registration End of registration
(2) Hierarchical registration – reliable points first
HAMMER
158 subjects Average Template
158 brains we used to construct average brain
Model brain
3D renderings
A subject before warping and after warping
Model
HAMMER in labeling brain structures:
Subject
HAMMER
- Cross-sectional views
Model Subject
HAMMER
Inner cortical surface
Outer cortical surface
Model Subject
- Label cortical surface
Registration – HAMMER
Template
Xue, Shen, et al., “Simulating Deformations of MR Brain Images for Evaluation of Registration Algorithms”, Neuroimage, Vol. 33: 855-866, 2006.
Simulating brain deformations for validating registration methods
Simulated
Successful applications of HAMMER:
10+ large clinical research studies and clinical trials involving >8,000 MR brain images:
• One of the largest longitudinal studies of aging in the world to date,
(an 18-year annual follow-up of 150 elderly individuals)
• A relatively large schizophrenia imaging study (148 participants)
• A morphometric study of XXY children
• The largest imaging study of the effects of diabetes on the brain to date,
(650 patients imaged twice in a 8-year period)
• A large study of the effects of organolead-exposure on the brain
• A study of effect of sustained, heavy drinking on the brain
Improving: Learning Best Features for Registration
Best-scale moments:
Wu, Qi, Shen, “Learning Best Features for Deformable Registration of MR Brains”, MICCAI, 2005.
Criteria for selecting best-scale moments of each point:• Maximally different from those of its nearby points. (Distinctiveness)• Consistent across different samples. (Consistency)• Best scales, used to calculate best-scale features, should be smooth spatially. (Regularization)
Moments w.r.t. scales:
Improving: Learning Best Features for Registration
• Visual improvement:
Model Ours HAMMER’s
Results:
• Average registration error:
Wu, Qi, Shen, “Learning-Based Deformable Registration of MR Brain Images”, IEEE Trans. Med. Imaging, 25(9):1145-1157, 2006.
Wu, Qi, Shen, “Learning Best Features and Deformation Statistics for Hierarchical Registration of MR Brain Images”, IPMI 2007.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Error 2mm
HAMMER
Improved method
0.66mm 0.95mm
Histogram of deformation estimation errors
Improving: Statistically-constrained HAMMER
Template
Statistical Model of
Deformations, using wavelet-
PCA
Registration
Subject
HAMMER
Normal brain deformation captured from 150 subjects
Xue, Shen, et al., “Statistical Representation of High-Dimensional Deformation Fields with Application to Statistically-Constrained 3D Warping”, Medical Image Analysis, 10:740-751, 2006.
Improving: Statistically-constrained HAMMER
Comparison of Histograms of Jacobian Determinants
0.0%
0.5%
1.0%
1.5%
2.0%
0 1 2 3 4
Jacobian Determinant
Per
cent
age
HAMMER
SMD+HAMMER
• More smooth deformations:Results:
• Detection on simulated atrophy:
HAMMER SMD+HAMMER
White Matter Lesion (WML) Segmentation
• WMLs are associated with cardiac and vascular disease, and may lead to different brain diseases, such as MS.
• Manual delineation
• Computer-assisted segmentation
WML Segmentation
- Fuzzy-connection- Multivariate Gaussian Model- Atlas based normal tissue distribution model- KNN based lesion detection
• 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.
T1
PD
• Image property: serious intensity overlap in WMLs
WML
FLAIR
T2
Our approach
Attribute Vector
• Attribute vector for each point v
},,,{,| 21 FLAIRPDTTmvttIvF mmm Neighborhood Ω (5x5x5mm)
T1T2PDFLAIR
• SVM To train a WML segmentation classifier.
• Adaboost To adaptively weight the training samples and improve the generalization of WML segmentation method.
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
Overview of Our Approach
Results
• Paired Spearman Correlation (SC)
Gold standard (rater 1)
Rater 2 Computer Mean+dev. of the lesion volume
Gold standard (rater 1) 1.0 0.95 0.79 1494+/-3416 mm3
Rater 2 0.95 1.0 0.74 2839+/-6192 mm3
Computer 0.79 0.74 1.0 1869+/-3400 mm3
Results – 45 Subjects
Double
• Coefficient of variation (CV)
Coefficient of Variation
Rater 1 189%
Rater 2 218%
Computer 182%
To investigate the variation of the lesion load’s distribution of the 35 evaluated subjects
Defined as CV=/.
Close
10 for training, and 35 for testing
• 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.
• Improve the robustness of multi-modality image registration (for T1/T2/PD/FLAIR) by using a novel quantitative and qualitative measurement for mutual information, where salient points will be considered more during the registration.
• Design region-adaptive classifiers, in order to allow each classifier for capturing relative simple WML intensity pattern in each region; we will also develop a WML atlas for guiding the WML segmentation.
Improvement in this project
• 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.
Conclusion
Further develop HAMMER registration and WML
segmentation algorithms improve their
robustness and performance
3D Slicer
Thank you!
http://bric.unc.edu/IDEAgroup/http://www.med.unc.edu/~dgshen/ IDE
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