1. Quantitative Brain Structure Analysis on MR Images
Zuyao Shan, Ph.D.
Division of Translational Imaging Research
Department of Radiological Sciences

2. Outline Introduction Cerebellum segmentation (Preliminary study)
Cortical structure segmentation
In the following slides, I would like first show you a brief summary of previous studies, most of them are based on adults. I will also show you why this quantitative brain structure becomes more challenging in our cases, i.e. pediatric brain images. Then a report on our preliminary study on cerebellum segmentation will be given. With this preliminary results, we obtained a grant support from Thrasher Research foundation to perform a study on cortical structure segmentation.

3. Brain Segmentation
With the ability to identify brain structures on MR images and to detect anatomic changes, the new volumetric tools aid in the diagnosis, treatment, and elucidation of changes associated with disease or abnormality.
Registration – based approaches
Pros: Straightforward tenet, robustness
Cons: Accuracy limited by match quality, mismatch leading to significant errors, relying on image only. One-one mapping may not existed, Speed
Deformable model – based approaches
Pros: Prior knowledge incorporated, high accuracy.
Cons: Good initialization needed, identification of landmarks
The newly emerging science of brain volumetrics, which is based on MR imaging, is facilitated by quantitative analysis of structures in the brain. It aids in diagnosis, for example, evaluate white matter lesion burden in multiple sclerosis (MS) patients; treatment such as radiation therapy, and elucidation of changes associated with disease or abnormality. There are two major type of method for brain structure segmentation currently, registration based and deformable model based methods, each has inherent advantages and disadvantages. Registration procedure can be linear or nonlinear, most studies use elastic or free-from transformation now. The registration can be based on intensity distribution or landmarks. Transformation can be designed as meshed grid, optical flow, viscous flow etc. for an example, a group of our neighbors, Vanderbilt University, used an free-form registration procedure based on mutual information. In deformable method, a parametric model consisting of a mean shape and several adjustment components is created from a set of training shapes. The adjustment components describe the principal modes of variation from the mean of the training set. New shapes can be generated by varying the weights of various components.

4. Brain Segmentation: inter-personal variability
More challenges in pediatric patients with brain tumors:
Removal of tissues
Different stages of development
An adequate method should cope with high inter-subject variability with high accuracy

5. Brain Segmentation: Cerebellum
Knowledge – guided active contour
Rigid-body registration: good initialization
Prior defined template: Knowledge incorporated
Active contour adjustment: high accuracy, robustness

6. Brain Segmentation: Cerebellum
Active contour (Snake): energy-minimizing spline

7. Brain Segmentation: Cerebellum
Active contour (Cont.): Internal energy
Small
Tension in the contour, low internal energy
High
Low
Bending in the contour, low internal energy
First derivative, second derivative

8. Brain Segmentation: Cerebellum
Active contour (Cont.): External energy
exponential
Sobel edge
Distance
detection
transform

9. Brain Segmentation: Cerebellum
Visual inspection

10. Brain Segmentation: Cerebellum
Visual inspection

11. Brain Segmentation: Cerebellum
Similarity evaluation
Kappa index
A vs. M1: ~ 0.94; A vs. M2: ~0.93; M1 vs. M2: 0.97
Compared with 0.77~0.84 for pediatric brain tumor patient in recent report1
S1∩ S2
D’Haese P et al. Int J Radiat Oncol Biol Phys 2003; 57 (2 Suppl): S205

12. Brain Segmentation: Cortical Structures
KAM, Knowledge-guided Active Model
New object functions
In contrast, Registration – based approaches maximize S; deformable model – based approaches minimize H
Pediatric brain atlas
Affine registration (H)
3D active mesh (S)

13. Brain Segmentation: Pediatric Brain Atlas

14. Brain Segmentation: Pediatric Brain Atlas

15. Brain Segmentation: Pediatric Brain Atlas

16. Brain Segmentation: Affine Registration
12 DOF: 3 translations, 3 rotations, 3 scaling, and 3 shearing

17. Brain Segmentation: Active Models
External Energy: attract triangle vertex to the edge of the image

18. Brain Segmentation: Active Models
Internal Energy: control the behavior of triangle mesh models

19. Brain Segmentation: Cortical Structures
Segmentation results

20. Brain Segmentation: Cortical Structures
Segmentation results

21. Brain Segmentation: Cortical Structures
Segmentation results compared with SPM2
Volumetric agreement:
KAM : 95.4% ± 3.7%
SPM2 : 90.4% ± 7.4%
Image similarities:
KAM : SPM2 : 0.86

22. Brain Segmentation: Summary
Pediatric brain atlas
KAM, Knowledge-guided Active Model
preliminary results indicate that when segmenting cortical structures, the KAM was in significantly better agreement with manually delineated structures than the nonlinear registration algorithm provided by SPM2.

23. Brain Segmentation: Future Studies
Validation of KAM
Application of KAM
Incorporating KAM into radiation therapy planning
Quantitative evaluation of cortical structure changes
Further development of KAM
Subcortical Structures
Brain Tumors

24. Acknowledgements Mentor: Dr. Wilburn E Reddick
Colleagues: Dr. Robert J Ogg
Dr. Fred H. Laningham
Dr. Claudia M. Hillenbrand
Carlos Parra, John Stagich, Dr. Qing Ji,
John Glass, Jinesh Jain, Travis Miller,
Rhonda Simmons