Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB006733 )

1. 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

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

3. Goal of this project • To further developHAMMER 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 the3D Slicer.

4. Overview of Our Brain Measurement Tools • To further developHAMMER registration and WML segmentation algorithms, for improving their robustness and performance. • To design separate software modules for these two algorithms and incorporate them into the3D Slicer.

5. HAMMER 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)

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

7. How can we detect correspondences? Difficulty: High variations of brain structures 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 Brain A Brain B Similarity Map Examples of attribute vector similarity maps, and point correspondences

9. Voxels with distinct attribute vectors. Roots of sulci All boundary voxels Crowns of gyri HAMMER (2) Hierarchical registration – reliable points first To minimize the effect of local minima • Few driving voxels • Smooth approximation of the energy function • Many driving voxels • Complete energy function

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

11. 158 brains we used to construct average brain Template 158 subjects Average

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

13. HAMMER HAMMER in labeling brain structures: Model Subject

14. HAMMER - Cross-sectional views Model Subject

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

16. Simulating brain deformations for validating registration methods Template Simulated • Xue, Shen, et al., “Simulating Deformations of MR Brain Images for Evaluation of Registration Algorithms”, Neuroimage, Vol. 33: 855-866, 2006.

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 • 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) Best-scale moments: Moments w.r.t. scales: • Wu, Qi, Shen, “Learning Best Features for Deformable Registration of MR Brains”, MICCAI, 2005.

19. Improving: Learning Best Features for Registration Results: • Visual improvement: Model Ours HAMMER’s • Average registration error: Histogram of deformation estimation errors • 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. Improved method HAMMER 0.66mm0.95mm

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 Results: • More smooth deformations: • Detection on simulated atrophy: HAMMER SMD+HAMMER

22. White Matter Lesion (WML) Segmentation

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

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

26. Overview of Our Approach Manual Segmentation Co-registration Skull-stripping Training SVM model via training sample and Adaboost Intensity normalization Training Pre-processing False positive elimination Voxel-wise evaluation & segmentation Testing Post-processing

27. Results

28. Results – 45 Subjects 10 for training, and 35 for testing • Paired Spearman Correlation (SC) Double • Coefficient of variation (CV) To investigate the variation of the lesion load’s distribution of the 35 evaluated subjects Defined as CV=/. Close • 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. Improvement in this project • 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. • 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 developHAMMER registration and WML segmentation algorithmsimprove their robustness and performance 3D Slicer

31. Image Display, Enhancement, and Analysis IDEA Thank you! http://bric.unc.edu/IDEAgroup/ http://www.med.unc.edu/~dgshen/