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

BMC Medical Imaging

Rajan Jain⁴  Tom Mikkelsen⁴  Mark Rosenblum²  Hamid Soltanian-Zadeh³  Quan Jiang⁵  Abbas Babajani-Fermi²  Sona Saksena¹  Mohammad-Reza Nazem-Zadeh⁵ 

[1] Department of Radiology, Henry Ford Health System, Detroit MI 48202, USA;Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis MO 63110, USA;Department of Radiology, Wayne State University, Detroit, MI 48202, USA;Department of Neurosurgery, Henry Ford Health System, Detroit, MI 48202, USA;Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA

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

Others  :  1092333
DOI  :  10.1186/1471-2342-12-10

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

【 摘 要 】

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).

【 授权许可】

   
2012 Nazem-Zadeh et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

【 参考文献 】

• [1]de Lacoste MC, Kirkpatrick JB, Ross ED: Topography of the human corpus callosum. J Neuropathol Exp Neurol 1985, 44:578-591.
• [2]Clarke S, Miklossy J: Occipital cortex in man: organization of callosal connections, related myelo- and cytoarchitecture, and putative boundaries of functional visual areas. J Comp Neurol 1990, 298:188-214.
• [3]Dorion A, Chantôme M, Hasboun D, et al.: Hemispheric asymmetry and corpus callosum morphometry: a magnetic resonance imaging study. Neuroscience Research 2000, 36:9-13.
• [4]Hasan K, Gupta R, Santos R, Wolinsky J, Narayana P: Diffusion tensor fractional anisotropy of the normal-appearing seven segments of the corpus callosum in healthy adults and relapsing-remitting multiple sclerosis patients. Journal of Magnetic Resonance Imaging 2005, 21:735-743.
• [5]Gupta R, Saksena S, Hasan K, et al.: Focal Wallerian degeneration of the corpus callosum in large middle cerebral artery stroke: serial diffusion tensor imaging. Journal of Magnetic Resonance Imaging 2006, 24:549-555.
• [6]Kubicki M, Styner M, Bouix S, et al.: Reduced interhemispheric connectivity in schizophrenia-tractography based segmentation of the corpus callosum. Schizophrenia research 2008, 106:125-131.
• [7]Rosas HD, Lee SY, Bender AC, Zaleta AK, Vangel M, Yu P, Fischl B, Pappu V, Onorato C, Cha JH, Salat DH, Hersch SM: Altered white matter microstructure in the corpus callosum in Huntington's disease: Implications for cortical. Neuroimage 2010, 49:2995-3004.
• [8]Lundervold A, Duta N: Taxt T. Jain A: Model-guided segmentation of corpus callosum in MR images. Citeseer 1999, 231-237.
• [9]Lee C, Huh S, Ketter T, Unser M: Automated segmentation of the corpus callosum in midsagittal brain magnetic resonance images. Optical Engineering 2000, 39:924-935.
• [10]Brejl M, Sonka M: Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples. IEEE Transactions on Medical Imaging 2000, 19:973-985.
• [11]Basser P, Mattiello J, Le Bihan D: MR diffusion tensor spectroscopy and imaging. Biophys 1994, 66:259-267.
• [12]Basser P, Mattiello J, Le Bihan D: Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson 1994, 103:247-254.
• [13]Brun A, Knutsson H, Park H, Shenton M, Westin C: Clustering fiber traces using normalized cuts. MICCAI LNCS 2004, 3216:368-375.
• [14]Maddah M, Grimson W, Warfield S, Wells W: A unified framework for clustering quantitative analysis of white matter fiber tracts. Med Imag Analysis 2008, 12:191-202.
• [15]Xu Q, Anderson A, Gore J, Ding Z: Unified Bundling and Registration of Brain White Matter Fibers. IEEE Transactions on Medical Imaging 2009, 28(9):1399-1411.
• [16]Voineskos A, Rajji T, Lobaugh N, et al.: Age-related decline in white matter tract integrity and cognitive performance: A DTI tractography and structural equation modeling study. Neurobiology of Aging 2010, in press. doi:10.1016/j.neurobiolaging.2010.02.009
• [17]Wassermann D, Bloy L, Kanterakis E, Verma R, Deriche R: Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers. Neuroimage 2010, 51(1):228-241.
• [18]Li H, Xue Z, Guo L, Liu T, Hunter J, Wong S: A hybrid approach to automatic clustering of white matter fibers. Neuroimage 2010, 49:1249-1258.
• [19]Eckstein I, Shattuck D, Stein J, et al.: Active fibers: Matching deformable tract templates to diffusion tensor images. Neuroimage 2009, 47:82-89.
• [20]Zhukov L, Museth K, Breen D, Whitaker R, Barr A: Level set modeling and segmentation of DT-MRI brain data. J Electron Imag 2003, 12:125-133.
• [21]Vemuri B, Chen Y, McGraw T, Wang Z, Mareci T: Fiber tract mapping from diffusion tensor MRI. Proc IEEE Workshop on Variational and Level Set Methods in Computer Vision 2001, 81-88.
• [22]Wang Z, Vemuri B: Tensor field segmentation using region based active contour model. ECCV LNCS 2004, 3024:304-315.
• [23]Rousson M, Lenglet C, Deriche R: Level set and region based surface propagation for diffusion tensor MRI segmentation. Computer vision and mathematical methods in medical and biomedical image analysis 2004, 123-134.
• [24]Feddern C, Weickert J, Burgeth B: Level set methods for tensor valued images. 2003, 65-72. Geometric and Level Set Methods in Computer Vision: In Proceedings of the Second IEEE Workshop on Variational
• [25]Jonasson L, Bresson X, Hagmann P, Cuisenaire O, Meuli R, Thiran J: White matter fiber tract segmentation in DT-MRI using geometric flows. Med Imag Analysis 2005, 3:223-236.
• [26]Jonasson L, Hagmann P, Pollo C, Bresson X, Wilson C, Meuli R, Thiran J: A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal Processing 2007, 87:309-321.
• [27]Lenglet C, Rousson M, Deriche R: A statistical framework for DTI segmentation. IEEE Trans Med Imag 2006, 25:675-700.
• [28]Arsigny V, Fillard P, Pennec X, Ayache N: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magnetic Resonance in Medicine 2006, 56:411-421.
• [29]Weldeselassie Y, Hamarneh G: DT-MRI segmentation using graph cuts. Citeseer: 65121 K-65121
• [30]Awate S, Hui Z, Gee J: A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: With applications to DTI-tract extraction. IEEE Transactions on Medical Imaging 2007, 26:1525-1536.
• [31]Hasan K, Ewing-Cobbs L, Kramer L, Fletcher J, Narayana P: Diffusion tensor quantification of the macrostructure and microstructure of human midsagittal corpus callosum across the lifespan. NMR in Biomedicine 2008, 21:1094-1101.
• [32]Jonasson L, Bresson X, Thiran J, Wedeen V, Hagmann P: Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts. IEEE Trans On Med Imaging 2007, 26:1547-1554.
• [33]Nazem-Zadeh M, Davoodi-Bojd E, Soltanian-Zadeh H: Level set fiber bundle segmentation using spherical harmonic coefficients. Computerized Medical Imaging and Graphics 2010, 34:192-202.
• [34]Nazem-Zadeh M, Davoodi-Bojd E, Soltanian-Zadeh H: Atlas-Based Fiber Bundle Segmentation Using Principal Diffusion Directions and Spherical Harmonic Coefficients. NeuroImage 2011, 54:246-164. Supplement 1
• [35]Descoteaux M, Deriche R: High angular resolution diffusion MRI segmentation using region-based statistical surface evolution. Journal of Mathematical Imaging and Vision 2008, 33(2):239-252.
• [36]Hagmann P, Jonasson L, Deffieux T, Meuli R, Thiran J, Wedeen V: Fibertract segmentation in position orientation space from high angular resolution diffusion MRI. Neuroimage 2006, 32:665-675.
• [37]McGraw T, Vemuri B, Yezierski R, Mareci T: Segmentation of High Angular Resolution Diffusion MRI modeled as a field of von mises-fisher mixtures. Proc European Conf on Computer Vision 2006, 463-475.
• [38]Hess C, Mukherjee P, Han E, Xu D, Vigneron D: Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn Reson Med 2006, 56:104-117.
• [39]Witelson S: Hand and sex differences in the isthmus and genu of the human corpus callosum: a postmortem morphological study. Brain 1989, 112:799.
• [40]Lee S, Cheng J, Chen C, Tseng W: An automatic segmentation approach for boundary delineation of corpus callosum based on cell competition. Proc Engineering in Medicine and Biology Society Conf of the IEEE 2008, 5514-5517.
• [41]Osher S, Sethian J: Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 1988, 79:12-49.
• [42]Osher S, Paragios N: Chapter: Level set methods. Geometric level set methods in imaging, vision, and graphics. Springer-Verlag New York Inc 2003, 3-20.
• [43]Dice LR: Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.
• [44]Wakana S, Caprihan A, Panzenboeck M, et al.: Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 2007, 36:630-644.
• [45]Zacharaki E, Hogea C, Shen D, Biros G, Davatzikos C: Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth. Neuroimage 2009, 46:762-774.