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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

Sensitivity of corpus callosum Witelson segments to Collinearity _Threshold for a normal subject. The graphs show number of True-Positives, number of False-Positives, and Dice correctness measure over a range of Collinearity _Threshold for the Witelson segments of the Corpus Callosum of a normal subject.
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Figure 3: Sensitivity of corpus callosum Witelson segments to Collinearity _Threshold for a normal subject. The graphs show number of True-Positives, number of False-Positives, and Dice correctness measure over a range of Collinearity _Threshold for the Witelson segments of the Corpus Callosum of a normal subject.

Mentions: Figure 3 shows the number of True-Positives, the number of False-Positives, and the Dice correctness measure over a range of Collinearity _Threshold for the Witelson segments of the Corpus Callosum of a normal subject. It is shown that when Collinearity _Threshold values are in a specific range (here 0.65 to 0.75), the Dice correctness measure is maximized. However, the sensitivity of the segmentation method to Collinearity _Threshold is less than its sensitivity to PDDx_Threshold. We found the following set of parameters optimal for segmenting corpus callosum: PDDx_Threshold = 0.55, Collinearity _Threshold = 0.7, FA_Threshold = 0.1, and F_Threshold = 0.05.


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)

Sensitivity of corpus callosum Witelson segments to Collinearity _Threshold for a normal subject. The graphs show number of True-Positives, number of False-Positives, and Dice correctness measure over a range of Collinearity _Threshold for the Witelson segments of the Corpus Callosum of a normal subject.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Sensitivity of corpus callosum Witelson segments to Collinearity _Threshold for a normal subject. The graphs show number of True-Positives, number of False-Positives, and Dice correctness measure over a range of Collinearity _Threshold for the Witelson segments of the Corpus Callosum of a normal subject.
Mentions: Figure 3 shows the number of True-Positives, the number of False-Positives, and the Dice correctness measure over a range of Collinearity _Threshold for the Witelson segments of the Corpus Callosum of a normal subject. It is shown that when Collinearity _Threshold values are in a specific range (here 0.65 to 0.75), the Dice correctness measure is maximized. However, the sensitivity of the segmentation method to Collinearity _Threshold is less than its sensitivity to PDDx_Threshold. We found the following set of parameters optimal for segmenting corpus callosum: PDDx_Threshold = 0.55, Collinearity _Threshold = 0.7, FA_Threshold = 0.1, and F_Threshold = 0.05.

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