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Validation of semiautomated methods for quantifying cingulate cortical metrics in schizophrenia
J Tilak Ratnanather1, Lei Wang, Mary Beth Nebel
1Center for Imaging Science, The Johns Hopkins University, Clark 301, 3400 North Charles St, Baltimore, MD 21218, USA. tilak@cis.jhu.edu
Psychiatry Research
|November 18, 2004
Summary
This study validates semiautomated methods for reconstructing cingulate gyrus cortical surfaces from MRI scans. The techniques provide accurate and reliable surface area measurements for neuroimaging research.
Area of Science:
- Neuroimaging
- Computational Anatomy
Background:
- Accurate reconstruction of brain structures like the cingulate gyrus is crucial for understanding neurological conditions.
- Manual segmentation of high-resolution magnetic resonance (MR) images is time-consuming and prone to inter-rater variability.
Purpose of the Study:
- To validate semiautomated methods for reconstructing cortical surfaces of the cingulate gyrus.
- To assess the accuracy and reliability of these methods for surface area quantification.
Main Methods:
- Bayesian segmentation and Neyman-Pearson Likelihood Ratio Test for tissue classification (CSF, GM, WM).
- Reassignment of partial volume voxels to minimize reconstruction error.
- Dynamic programming for cingulate gyrus delineation based on gyral and sulcal boundaries.
Main Results:
- Generated topology-correct cortical surfaces with vertices within 0.5 mm of hand contours.
- Achieved high inter-rater reliability for surface area (intraclass correlation coefficient = 0.82).
- Demonstrated low intra-subject variability (coefficient of variation = 0.0438) upon repeated analysis.
Conclusions:
- Semiautomated methods provide a validated and reliable approach for cingulate gyrus cortical surface reconstruction.
- These methods offer improved accuracy and efficiency compared to manual techniques in neuroimaging studies.
- The validated approach can be beneficial for quantitative analysis in clinical and research settings.