An experimental evaluation of diffusion tensor image segmentation using graph-cuts.

The segmentation of diffusion tensor imaging (DTI) data is a challenging problem due to the high variation and overlap of the distributions induced by individual DTI measures (e.g., fractional anisotropy). Accurate tissue segmentation from DTI data is important for characterizing the mi-crostructural properties of white matter (WM) in a subsequent analysis. This step may also be useful for generating a mask to constrain the results of WM tractography. In this study, a graph-cuts segmentation method was applied to the problem of extracting WM, gray matter (GM) and cerebral spinal fluid (CSF) from brain DTI data. A two-phase segmentation method was adopted by first segmenting CSF signal from the DTI data using the third eigenvalue (lambda(3)) maps, and then extracting WM regions from the fractional anisotropy (FA) maps. The algorithm was evaluated on ten real DTI data sets obtained from in vivo human brains and the results were compared against manual segmentation by an expert. Overall, the graph cuts method performed well, giving an average segmentation accuracy of about 0.90, 0.77 and 0.88 for WM, GM and CSF respectively in terms of volume overlap(VO).

A population-average MRI-based atlas collection of the rhesus macaque.

Magnetic resonance imaging (MRI) studies of non-human primates are becoming increasingly common; however, the well-developed voxel-based methodologies used in human studies are not readily applied to non-human primates. In the present study, we create a population-average MRI-based atlas collection for the rhesus macaque (Macaca mulatta) that can be used with common brain mapping packages such as SPM or FSL. In addition to creating a publicly available T1-weighted atlas (http://www.brainmap.wisc.edu/monkey.html), probabilistic tissue classification maps and T2-weighted atlases were also created. Theses atlases are aligned to the MRI volume from the Saleem, K.S. and Logothetis, N.K. (2006) atlas providing an explicit link to histological sections. Additionally, we have created a transform to integrate these atlases with the F99 surface-based atlas in CARET. It is anticipated that these tools will help facilitate voxel-based imaging methodologies in non-human primate species, which in turn may increase our understanding of brain function, development, and evolution.