1. Cortical Surface Analysis and Automatic Parcellation of Human Brain Wen Li, Ph.D. student Advisor: Vincent A. Magnotta, Ph.D. Biomedical Engineering University of Iowa, Iowa City, IA, USA

2. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

3. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

4. BRAINS (Brain Research: Analysis of Images, Networks and Systems http://www.nitrc.org/projects/brains) http://www.nitrc.org/projects/brains Topographic structures vs. functions of human cerebral cortex Image-based vs. surface-based human cortical analysis Automatic, rapid and reliable parcellation of cerebral cortex (development, aging, disease progression or treatment response)

5. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

6. BRAINS AutoWorkup AC-PC alignment T 1 -T 2 image registration Skull stripping/Brain masking Tissue classification left-right hemisphere separation Removal of cerebellum and brainstem Image size: 256x256x192 pixel 3 Voxel size: 1.0x1.0x1.0 mm 3 Intensity range: 8 bits (0~255) 10: pure CSF 130: pure GM 250: pure WM

7. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

8. Topology Correction (Neurolib) Marching Cubes Surface Decimation (220,000 -> 70,000) Surface Smoothing

9. Geometry features Distance from surface point to posterior-commissure [-1.0, 1.0] Distance from surface point to a convex hull of it [0.0, 1.0] Mean curvature [-1.0, 1.0]

10. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

11. Linear parameterizations with fixed boundaries (itk::QuadEdgeMeshToSphereFilter) Split surface into halvesBoundary smoothingResulting sphere Spheres with geometry features of original surface

12. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

13. Modified diffeomorphic demons registration Velocity field is not an arbitrary 3D vector field. It is a tangent vector field on the sphere. Spherical Diffeomorphic Demons –Reference: B.T.T. Yeo et al. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650--668, 2010.

14. Muti-resolution spherical demons registration 4 resolution levels (Icosahedral regular spheres) levelResolutionFeatureSize (cells) 1IC4 DistanceFrom PC 5,120 2IC5GeoDepth20,480 3IC6GeoDepth81,920 4IC7 Mean Curvature 327,680 B.T.T. Yeo IEEE Transactions on Medical Imaging, 29(3):650--668, 2010.

15. Fixed mesh Moving mesh Level 1Level 2Level 3Level 4 Deformation field Labels on moving mesh Warped labels

16. Two common indexes to calculate similarity (overlapping) of region A and B Dice Index: Jaccard Index:

17. Evaluation by using Dice Index The average Dice across subjects and regions is 0.88. SubjectLabel 1Label 2Label 3Label 4Average 10.9750.9010.8860.8870.912 20.9530.870 0.8830.894 30.9570.7870.8380.8530.859 40.9170.8610.7620.8660.852 50.9520.8590.8660.8670.886

18. Outline Background Preprocessing Surface Generation Conformal Flattening Spherical Demons Registration Conclusion

19. This pipeline proves to be capable of parcellating the surface of human cortex automatically in reasonable time Applying Spherical Demons Registration in pyramid levels helps implement the registration by different geometry features in different resolution levels

20. We are currently evaluating the approach for a more refined cortical parcellation. 50 subjects’ data will be applied in the pipeline and further results will be given soon.