Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI

MacDonald, David; Kabani, Noor Jehan; Avis, David; Evans, Alan C.

doi:10.1006/nimg.1999.0534

Cited by 783 publications

(670 citation statements)

References 24 publications

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“…Alternatively, with WM and pial boundaries defined, both boundaries can be deformed simultaneously using either snake like deformable models (MacDonald et al, 2000;Kim et al, 2005) or level sets (Zheng et al, 1999), thereby utilising distance constraints to ensure a realistic coupling of the two surfaces. The use of explicit surface models enables sub-voxel accuracy (Fischl and Dale, 2000), high sensitivity , and robustness to different field strengths, scanner upgrade and scanner manufacturer (Han et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Surface based cortical thickness methods try to ensure correct topology of the surface after initial segmentation of the WM boundary (Shattuck and Leahy, 2001;Xu et al, 1999;Han et al, 2004), using smoothness and self intersection constraints MacDonald et al, 2000), by correcting topological defects as they occur (Fischl et al, 2001;Segonne et al, 2005), or using a Laplacian function (Kim et al, 2005). Ensuring correct topology or surface regularity massively increases computational cost (Fischl et al, 2001;Han et al, 2004), may require a difficult balance of parameter weights (Kim et al, 2005;Scott et al, 2009), and reduces the model's ability to follow areas of high curvature such as extremely thin gyral stalks (Lohmann et al, 2003) or opposing sides of sulci with no clear CSF between, which can produce bias and error in thickness measurements (Scott et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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A comparison of voxel and surface based cortical thickness estimation methods

et al. 2011

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Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison of voxel and surface based cortical thickness estimation methods

et al. 2011

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“…These surface properties-topologically equivalent to a sphere, smooth, and having no selfintersections-are difficult to maintain simultaneously in practice. Reconstructing a smooth surface is usually accomplished by using a deformable surface model having internal btensionQ or bbendingQ forces (Dale et al, 1999;Goldenberg et al, 2002;MacDonald et al, 2000;Xu et al, 1999;Zeng et al, 1999). We follow this approach here as well.…”

Section: Introductionmentioning

confidence: 99%

“…We follow this approach here as well. Generating a topologically correct cortical surface can be accomplished by first finding an initial surface with the correct topology and then preserving the topology during surface evolution using a parametric deformable model (Dale et al, 1999;MacDonald et al, 2000;Xu et al, 1999). The deforming surface in parametric deformable models, however, can easily develop self-intersections; the detection and prevention of which is usually very time-consuming (Dale et al, 1999;MacDonald et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

CRUISE: Cortical reconstruction using implicit surface evolution

et al. 2004

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Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from selfintersections. Validation results on real MR data are presented to demonstrate the performance of the method. D 2004 Elsevier Inc. All rights reserved.

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“…For quantitative evaluation, however, it is necessary to establish a coordinate system on the cortex so that features can be identified more rigorously (Angenent et al, 1999;Drury et al, 1996;Fischl et al, 1999;Hurdal et al, 1999;MacDonald et al, 2000;Thompson et al, 2000). Spherical coordinates are appropriate since the topology of the sphere is the same as that of the cortical surface.…”

Section: Spherical Mappingmentioning

confidence: 99%

Cortical surface segmentation and mapping

et al. 2004

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Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuro-science and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.

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Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI

Cited by 783 publications

References 24 publications

A comparison of voxel and surface based cortical thickness estimation methods

A comparison of voxel and surface based cortical thickness estimation methods

CRUISE: Cortical reconstruction using implicit surface evolution

Cortical surface segmentation and mapping

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