Automatic Brain Segmentation in Automatic Brain Segmentation in Rhesus MonkeysRhesus Monkeys

February 2006, SPIE Medical Imaging 2006

Funding provided by UNC Neurodevelopmental Disorders Research Center HD 03110 as well as the NIH AI067518 (Maternal flu infection and brain development in primates).REFERENCES[1] D. Van Essen, H. Drury, S. Joshi, and M. Miller, Comparisons between human and macaque using shape-based deformation algorithms applied to cortical flat maps, Functional Mapping of the Human Brain, p. 41., 1997[2] D. Van. Essen, Surface-based approaches to spatial localization and registration in primate cerebral cortex,NeuroImage 23 Suppl 1, pp. 97-107, 2004[3] S. Joshi, B. Davis, M. Jomier, and G. Gerig, Unbiased diffeomorphic atlas construction for computational anatomy, NeuroImage 23, pp. 151-S160, 2004[4] M. Styner, H. C. Charles, J. Park, J. Lieberman, and G. Gerig, Multi-site validation of image analysis methods - assessing intra and inter-site variability,SPIE Medical Imaging, 4684, pp. 278-286, 2002.[5] M. Prastawa, J. H. Gilmore, and W. L. and Guido Gerig, Automatic segmentation of mr images of the developing newborn brain, Medical Image Analysis 9, pp. 457 - 466, October 2005

Data

Fig. 2: Left: Scheme of unbiased atlas image computation by jointly deforming a training set into their average image. Right: Computed atlas and a selected training image at similar slices. The gain in SNR is clearly visible.

Summary: We built an atlas for the pediatric rhesus monkey brain for use in the automatic segmentation of brain tissue, lobar parcellation and subcortical structures in MRI datasets. We have applied the segmentation successfully in three studies of rhesus monkey brain development.

• Neuroimaging applied to primates, rhesus monkeys• Pathologies/environment studied in well-controlled settings• Brain development in intra-uterine exposure models• Brain tissue, lobar parcellation, structures, cortical thickness• Surface based analysis of Van Essen et al. [1,2] complements ours,

combination with structures and cortical thickness

Introduction

Martin Styner1,2, Rebecca Knickmeyer2, Sarang Joshi3, Christopher Coe4, Sarah J Short4, John Gilmore2

1 Department of Computer Science, 2 Department of Psychiatry, University of North Carolina at Chapel Hill, 3 Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA

4 Department of Psychology, University of Wisconsin, Madison, WI, USA

Fig. 1: . Left: Transparent rhesus monkey head (adolescent, age 34m) with brain GM surface segmentation from superior viewpoint. Middle: Structural segmentation. Right: Cortical thickness visualization.

Fig. 4: Results on representative example. Left: Tissue segmentation. Right: Lobar parcellation.

Fig. 3: Lobar parcellation definition shown on representative example. Left on gray/pial matter surface, right on WM/GM boundary. Top row: outer boundary as seen from a lateral viewpoint, bottom row: only right hemisphere as seen from a medial viewpoint.

Segmentation • Tissue segmentation into WM/GM/CSF (probabilistic & binary)

• Adaptation of existing tool for human images [5]• Atlas based EM segmentation, stable, reliable [4]• Uses both T1w and T2w images• Skull stripping, inhomogeneity correction

• Intensity calibration of T1w matching tissue statistics via spline based histogram transfer function

• Parcellation computation on T1w • Fluid deformation (same as atlas building[3]) of atlas to image• Apply deformation to parcellation and mask tissue segmentation• Same deformation map can be applied to subcortical definitions

• Original atlas for 16-34m, successfully applied over 100 datasets in range 9-64months

• Whole segmentation pipeline is automatic, deterministic• Next steps: validation, structural segmentation, cortical thickness

Conclusions

• Training population of 18 development subjects (Macaca mulatta, age 16 - 34 months) for a single image atlas

• Application to over 50 additional subjects (ages 9 to 64m)• Subjects from 500+ breeding colony at Harlow Primate Lab, Uwisc• Scans: 3T GE (Signa), 3D-SPGR (0.23x0.23x0.5mm3), T2w SE

(0.23x0.23x1.5mm3), 12-directional DTI (not used here)

1. Initial affine atlas: Rigid + affine registration to single template, intensity calibration with histogram quantile matching, voxel-wise atlas for initial skull stripping and re-registration

2. Unbiased atlas image from training population by iterative diffeomorphic fluid registration to average with minimal distortion[3]

3. Initial white matter(WM), gray matter(GM), cerebrospinal fluid(CSF) maps from smoothed, edited thresholds: WM/GM on T1, CSF on T2

4. Atlas based tissue segmentation of training set [4,5]1. Inhomogeneity correction, skull stripping, intensity calibration2. Probabilistic tissue maps by averaging, atlas re-computation

5. Manual definition of lobar subdivision on atlas on left, flipping to right, and manual editing of right

• Generation and application of rhesus monkey brain atlas for tissue classification and regional parcellation

• Atlas: Cortical variability is limited, enhanced SNR, appropriate for the intermediate developmental stages up to early adult age.

• Deformable registration identifies corresponding cortical gyri.

Atlas Computation