Email updates

Keep up to date with the latest news and content from BMC Medical Imaging and BioMed Central.

Open Access Research article

Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma

Mohammad-Reza Nazem-Zadeh123, Sona Saksena4, Abbas Babajani-Fermi45, Quan Jiang3, Hamid Soltanian-Zadeh146*, Mark Rosenblum5, Tom Mikkelsen7 and Rajan Jain47

Author Affiliations

1 Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 14399, Iran

2 Department of Radiation Oncology and Radiology, University of Michigan, Ann Arbor, MI 48109-0010, USA

3 Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA

4 Department of Radiology, Henry Ford Health System, Detroit MI 48202, USA

5 Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis MO 63110, USA

6 Department of Radiology, Wayne State University, Detroit, MI 48202, USA

7 Department of Neurosurgery, Henry Ford Health System, Detroit, MI 48202, USA

For all author emails, please log on.

BMC Medical Imaging 2012, 12:10  doi:10.1186/1471-2342-12-10

Published: 16 May 2012

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).

Keywords:
Corpus callosum; Fiber bundle segmentation; Level-set; Glioblastoma; Diffusion tensor imaging