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

BMC Medical Imaging

Research Article

Mohammad-Reza Nazem-Zadeh¹  Hamid Soltanian-Zadeh²  Quan Jiang³  Tom Mikkelsen⁴  Sona Saksena⁵  Rajan Jain⁶  Abbas Babajani-Fermi⁷  Mark Rosenblum⁸ 

[1] Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, 14399, Tehran, Iran;Department of Radiation Oncology and Radiology, University of Michigan, 48109-0010, Ann Arbor, MI, USA;Department of Neurology, Henry Ford Health System, 48202, Detroit, MI, USA;Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, 14399, Tehran, Iran;Department of Radiology, Henry Ford Health System, 48202, Detroit, MI, USA;Department of Radiology, Wayne State University, 48202, Detroit, MI, USA;Department of Neurology, Henry Ford Health System, 48202, Detroit, MI, USA;Department of Neurosurgery, Henry Ford Health System, 48202, Detroit, MI, USA;Department of Radiology, Henry Ford Health System, 48202, Detroit, MI, USA;Department of Radiology, Henry Ford Health System, 48202, Detroit, MI, USA;Department of Neurosurgery, Henry Ford Health System, 48202, Detroit, MI, USA;Department of Radiology, Henry Ford Health System, 48202, Detroit, MI, USA;Mallinckrodt Institute of Radiology, Washington University School of Medicine, 63110, St. Louis, MO, USA;Mallinckrodt Institute of Radiology, Washington University School of Medicine, 63110, St. Louis, MO, USA;

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

DOI  :  10.1186/1471-2342-12-10

 received in 2011-08-08, accepted in 2012-05-16,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundThis paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma.MethodsNineteen 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.ResultsDice 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.ConclusionsThe 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).

【 授权许可】

Unknown   
© Nazem-Zadeh et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.