Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique

Background. There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncother-apy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods. DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and aniso-tropic diffusion characteristics. Results. Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each seg-ment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Sup-port vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions. D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brai