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

Nazem-Zadeh MR, Saksena S, Babajani-Fermi A, Jiang Q, Soltanian-Zadeh H, Rosenblum M, Mikkelsen T, Jain R - BMC Med Imaging (2012)

Bottom Line: In this algorithm, diffusion pattern of corpus callosum was used as prior information.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.

View Article: PubMed Central - HTML - PubMed

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

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).

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Related in: MedlinePlus

Segmentation results by the proposed method for a normal corpus callosum and its neighbors rotated 30° under the elevation angle. (a), (b): Segmented corpus callosum in green overlaid on the T1 sagittal and axial images, respectively. (c), (d): Boundaries of the corpus callosum delineated in white in the sagittal and axial slices, respectively, overlaid on the color coding of the principal diffusion direction in each pixel.
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Figure 9: Segmentation results by the proposed method for a normal corpus callosum and its neighbors rotated 30° under the elevation angle. (a), (b): Segmented corpus callosum in green overlaid on the T1 sagittal and axial images, respectively. (c), (d): Boundaries of the corpus callosum delineated in white in the sagittal and axial slices, respectively, overlaid on the color coding of the principal diffusion direction in each pixel.

Mentions: We used the Wilcoxon two sample tests to compare sensitivity of the outer subdivisions (rostrum, genu, and splenium) and the inner subdivisions (rostral body, anterior and posterior mid-body, and isthmus) of corpus callosum with respect to the rotation. For two arrays A and B, the Wilcoxon test performs a paired two-sided signed rank test of the hypothesis that data in the vector A-B come from a symmetric distribution with zero median. P-values less than 0.01 are considered statistically significant. From the p-values in Table 4, it can be inferred that the outer subdivisions have significantly lower average Dice measures than the inner subdivision for all azimuth and elevation rotation angles (p-values less than 0.0004). For skew angles, however, the outer subdivisions do not show significantly lower average Dice measures than the inner subdivision (5 out of 6 p-values are more than 0.01). Considering the overall diffusivity inside corpus callosum perpendicular to its main axis, the overall diffusivity pattern changes more dramatically around the x and y axes (azimuth and elevation) than around the z axis (skew). Therefore, the segmentation results are more sensitive to rotations in the azimuth and elevation directions. Figures 8, 9 and 10 show the segmentation results of the proposed method for the corpus callosum and its neighbors rotated 30° in the azimuth, elevation, and skew directions, respectively.


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

Nazem-Zadeh MR, Saksena S, Babajani-Fermi A, Jiang Q, Soltanian-Zadeh H, Rosenblum M, Mikkelsen T, Jain R - BMC Med Imaging (2012)

Segmentation results by the proposed method for a normal corpus callosum and its neighbors rotated 30° under the elevation angle. (a), (b): Segmented corpus callosum in green overlaid on the T1 sagittal and axial images, respectively. (c), (d): Boundaries of the corpus callosum delineated in white in the sagittal and axial slices, respectively, overlaid on the color coding of the principal diffusion direction in each pixel.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368740&req=5

Figure 9: Segmentation results by the proposed method for a normal corpus callosum and its neighbors rotated 30° under the elevation angle. (a), (b): Segmented corpus callosum in green overlaid on the T1 sagittal and axial images, respectively. (c), (d): Boundaries of the corpus callosum delineated in white in the sagittal and axial slices, respectively, overlaid on the color coding of the principal diffusion direction in each pixel.
Mentions: We used the Wilcoxon two sample tests to compare sensitivity of the outer subdivisions (rostrum, genu, and splenium) and the inner subdivisions (rostral body, anterior and posterior mid-body, and isthmus) of corpus callosum with respect to the rotation. For two arrays A and B, the Wilcoxon test performs a paired two-sided signed rank test of the hypothesis that data in the vector A-B come from a symmetric distribution with zero median. P-values less than 0.01 are considered statistically significant. From the p-values in Table 4, it can be inferred that the outer subdivisions have significantly lower average Dice measures than the inner subdivision for all azimuth and elevation rotation angles (p-values less than 0.0004). For skew angles, however, the outer subdivisions do not show significantly lower average Dice measures than the inner subdivision (5 out of 6 p-values are more than 0.01). Considering the overall diffusivity inside corpus callosum perpendicular to its main axis, the overall diffusivity pattern changes more dramatically around the x and y axes (azimuth and elevation) than around the z axis (skew). Therefore, the segmentation results are more sensitive to rotations in the azimuth and elevation directions. Figures 8, 9 and 10 show the segmentation results of the proposed method for the corpus callosum and its neighbors rotated 30° in the azimuth, elevation, and skew directions, respectively.

Bottom Line: In this algorithm, diffusion pattern of corpus callosum was used as prior information.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.

View Article: PubMed Central - HTML - PubMed

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

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).

Show MeSH
Related in: MedlinePlus