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 (lambda₃) 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).