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

Nazem-Zadeh et al. BMC Medical Imaging 2012, 12:10 http://www.biomedcentral.com/1471-2342/12/10 RESEARCH ARTICLE Open Access Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma Mohammad-Reza Nazem-Zadeh1,2,3, Sona Saksena4, Abbas Babajani-Fermi4,5, Quan Jiang3, Hamid Soltanian-Zadeh1,4,6*, Mark Rosenblum5, Tom Mikkelsen7 and Rajan Jain4,7 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 hypersurface 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 Background Corpus callosum is the largest inter-hemispheric fiber bundle in the human brain [1,2]. 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 [3]. Previous studies have mainly investigated effects of various pathologies on the corpus callosum [4-7]. However, a fully automated, fast, and accurate method for segmenting corpus callosum without penetrating into irrelevant neighboring * Correspondence: 1 Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 14399, Iran Full list of author information is available at the end of the article structures, using data acquired in routine clinical protocols, is still lacking. Previously, image processing methods have been proposed for segmenting corpus callosum in anatomical magnetic resonance images (MRI) [8-10]. These methods rely on intensity information of two-dimensional images and their results may need pruning. Recently, attention has been oriented towards diffusion tensor imaging (DTI) to segment white matter tracts of the brain [11,12]. 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. When using DTI data, the fiber bundles can be © 2012 Nazem-Zadeh et al; licensee 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. Nazem-Zadeh et al. BMC Medical Imaging 2012, 12:10 http://www.biomedcentral.com/1471-2342/12/10 extracted by: a) clustering of fibers resulting from tractography into fiber bundles [13-19]; or b) segmenting fiber bundles via hyper-surface propagation based on local properties of diffusion tensor, diffusion signal, or orientation distribution function (ODF) [20-27]. Since clustering methods rely on the tractography results, they do not work properly if the tractography results are inaccurate. On the other hand, segmentation methods based on hyper-surface propagation do not use the tractography results and are thus more robust to noise. In a region-based segmentation framework, a similarity measure between successive tensors is typically used. Some of the hyper-surface propagating methods in the literature concentrated on scalar quantities derived from the tensor data which do not reflect complete tensor information [20]. Other methods benefit from the entire information contained in the DTI data [21-30]. Wang and Vemuri [22] proposed a statistical level-set segmentation method. However, the tensors derived in this framework are not necessarily positive semi-definite, leading to inappropriate results especially when consecutive tensors are much different. Metrics like Kullback-Leibler divergence and J-divergence [23,24] have also been proposed. One of the most promising methods is introduced by Jonasson et al. [25]. They defined a new similarity measure called normalized tensor scalar product (NTSP). Comparing NTSP with other similarity measures, they demonstrated superiority of their proposed measure. To segment brain structures like thalamic nuclei, they modified their framework to favor the propagation of multiple hyper surfaces without overlapping [26]. Lenglet et al. [27] defined a dissimilarity measure and statistics between tensors based on the Riemannian distances. Although improving the segmentation results, this approach is computationally expensive. Defining a Log-Euclidean distance, another metric was defined by Arsigny et al. [28] which has lower computational burden. Weldeselassie and Hamarneh [29] used their proposed similarity measure in an energy minimization framework. Awate et al. [30] used the similarity measure in a Markov random field framework. In terms of quantitative evaluation of diffusion parameters, previous studies have compared DTI-based indices in normal appearing white matter and corpus callosum in multiple sclerosis [4], stroke [5], schizophrenia [6], and Huntington’s [7] and also studied the DTI methods to assess corpus callosum regions across the human lifespan [31]. For segmenting corpus callosum and its subdivisions in these studies, however, two-dimensional (2D) methods were applied and DTI-based indices compared in the midsagittal plane. However, without recruiting a three dimensional (3D) method to segment the whole corpus callosum and its subdivisions, the extracted quantities may be inaccurate. Page 2 of 16 Since the tensor model is not capable of describing heterogeneou (...truncated)