![]() Although the tensor model fails to describe higher order anisotropies in heterogeneous areas where more than one fiber population exists, it is practically useful for extracting major white matter tracts, particularly the ones with predominant diffusivity pattern such as corpus callosum. Recently, attention has been oriented towards diffusion tensor imaging (DTI) to segment white matter tracts of the brain. These methods rely on intensity information of two-dimensional images and their results may need pruning. Previously, image processing methods have been proposed for segmenting corpus callosum in anatomical magnetic resonance images (MRI). ![]() However, a fully automated, fast, and accurate method for segmenting corpus callosum without penetrating into irrelevant neighboring structures, using data acquired in routine clinical protocols, is still lacking. Previous studies have mainly investigated effects of various pathologies on the corpus callosum. Most of the fibers interconnect homologue cortical areas in roughly mirror image sites but a large number of the fibers have heterotypic connections ending in asymmetrical areas. The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity).Ĭorpus callosum is the largest inter-hemispheric fiber bundle in the human brain. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results. Resultsĭice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. In this algorithm, diffusion pattern of corpus callosum was used as prior information. ![]() Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma. ![]()
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