title: Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma.
abstract: BACKGROUND
  This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma.
  
  
  METHODS
  Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases.
  
  
  RESULTS
  Dice 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. 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.
  
  
  CONCLUSIONS
  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).
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 14399, Iran.
authors.forename: Mohammad-Reza
authors.initials: MR
authors.lastname: Nazem-Zadeh
authors.forename: Sona
authors.initials: S
authors.lastname: Saksena
authors.forename: Abbas
authors.initials: A
authors.lastname: Babajani-Fermi
authors.forename: Quan
authors.initials: Q
authors.lastname: Jiang
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
authors.forename: Mark
authors.initials: M
authors.lastname: Rosenblum
authors.forename: Tom
authors.initials: T
authors.lastname: Mikkelsen
authors.forename: Rajan
authors.initials: R
authors.lastname: Jain
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D003337:Corpus Callosum / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D005909:Glioblastoma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique