1. Dinggang Shen Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB006733 ) Department of Radiology and BRIC UNC-Chapel Hill IDEA

2. Team UNC-Chapel Hill - Dinggang Shen - 1/2 Postdoctoral fellow(s) UPenn - Christos Davatzikos GE - Jim Miller - Xiaodong Tao

3. 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.

4. 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.

5. 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

6. Model: Individual: (1) Formulated as correspondence detection Registration – HAMMER

7. 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.

8. 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

9. (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 gyri All boundary voxels

10. Beginning of registrationEnd of registration (2) Hierarchical registration – reliable points first HAMMER

11. 158 subjectsAverage Template 158 brains we used to construct average brain

12. Model brain 3D renderings A subject before warping and after warping

13. Model HAMMER in labeling brain structures: Subject HAMMER

14. - Cross-sectional views Model Subject HAMMER

15. Inner cortical surface Outer cortical surface Model Subject - Label cortical surface Registration – HAMMER

16. 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

17. 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

18. 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:

19. 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. HAMMER Improved method 0.66mm 0.95mm Histogram of deformation estimation errors

20. Improving: Statistically-constrained HAMMER 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.

21. Improving: Statistically-constrained HAMMER More smooth deformations: Results: Detection on simulated atrophy: HAMMER SMD+HAMMER

22. White Matter Lesion (WML) Segmentation

23. 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.

24. T1 PD Image property: serious intensity overlap in WMLs WML FLAIR T2 Our approach

25. Attribute Vector Attribute vector for each point v Neighborhood Ω (5x5x5mm) T1 T2 PD FLAIR SVM  To train a WML segmentation classifier. Adaboost  To adaptively weight the training samples and improve the generalization of WML segmentation method.

26. 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

27. Results

28. Paired Spearman Correlation (SC) Gold standard (rater 1) Rater 2ComputerMean+dev. of the lesion volume Gold standard (rater 1)1.00.950.791494+/-3416 mm 3 Rater 20.951.00.742839+/-6192 mm 3 Computer0.790.741.01869+/-3400 mm 3 Results – 45 Subjects Double Coefficient of variation (CV) Coefficient of Variation Rater 1189% Rater 2218% Computer182% 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.

29. 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.

30. Conclusion Further develop HAMMER registration and WML segmentation algorithms  improve their robustness and performance 3D Slicer

31. Thank you! http://bric.unc.edu/IDEAgroup/ http://www.med.unc.edu/~dgshen/ IDEA