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

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.

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.

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

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

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

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

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

Brain Segmentation: Cerebellum Visual inspection

Brain Segmentation: Cerebellum Visual inspection

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

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)

Brain Segmentation: Pediatric Brain Atlas

Brain Segmentation: Pediatric Brain Atlas

Brain Segmentation: Pediatric Brain Atlas

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

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

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

Brain Segmentation: Cortical Structures Segmentation results

Brain Segmentation: Cortical Structures Segmentation results

Brain Segmentation: Cortical Structures Segmentation results compared with SPM2 Volumetric agreement: KAM : 95.4% ± 3.7% SPM2 : 90.4% ± 7.4% Image similarities: KAM : 0.95 SPM2 : 0.86

Brain Segmentation: Summary Pediatric brain atlas www.stjude.org/brainatlas 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.

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

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