Sulcal Segmentation for Cortical Thickness Measurements

Hutton, Chloe; Vita, Enrico De; Turner, Robert

doi:10.1007/3-540-45786-0_55

Cited by 8 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, voxel-based techniques (Hutton et al, 2008; Diep et al, 2007; Lohmann et al, 2003; Srivastava et al, 2003; Hutton et al, 2002) operate directly on the 3D voxel grid of the image, and are therefore more computationally efficient. Those methods are however less robust to noise and mis-segmentation as they typically lack the mechanisms required to assess and correct topological errors.…”

Section: Introductionmentioning

confidence: 99%

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps

Acosta

Bourgeat

Zuluaga

et al. 2009

Medical Image Analysis

View full text Add to dashboard Cite

Accurate cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy. The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data. Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimer's disease (AD) patients were found on clinical data from the ADNI database. We compared our method in terms of precision, computational speed and statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our method the number of samples is reduced by 25% to find significant differences between the two groups.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps

Acosta

Bourgeat

Zuluaga

et al. 2009

Medical Image Analysis

View full text Add to dashboard Cite

show abstract

“…The problem of detecting buried sulci to improve thickness measurements has been addressed explicitly by many researchers (Hutton et al, 2002; Jones et al, 2000; Scott and Thacker, 2005; Lohmann et al, 2003; Barta et al, 2005; Han et al, 2004), often in the form of a pre or post-processing step. On the other hand, labeling cortical sulci directly from MRI volumes (Tu et al, 2007), or from surface models (Sandor and Leahy, 1997) is a well-studied problem in itself.…”

Section: Discussionmentioning

confidence: 99%

“…Such constraints increase reliability and performance in the presence of noise and when image resolution is sub-optimal. von Economo (1929) reported cortical thickness to be between 1.2–4.5 millimeters (mm) from ex vivo measurements, and ≈5 mm is generally reported to be the maximum observed value from in vivo measurements (Fischl and Dale, 2000; Kabani et al, 2001; Hutton et al, 2002). Some surface reconstruction based methods employ proximity constraints on the distance between two surfaces to ensure thickness lies within an anatomic range (Zeng et al, 1999; MacDonald et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Note that we use the terms buried cortex and buried sulci to refer to these regions of unresolved CSF, as has been used by other studies of cortical thickness (Kim et al, 2005; Hutton et al, 2008), as opposed to referring to the cortex in deep sulcal regions in general (with CSF separating the sulcal banks resolved or not) that is termed buried rather than cortex visible on the outer surface , as described in Van Essen et al (1998); Rettmann et al (2006). Several existing approaches explicitly address this problem: by image enhancement (Jones et al, 2000) to recover CSF, using morphological processing (Lohmann et al, 2003), stochastic modeling (Barta et al, 2005) and explicit image-based labeling of sulcal points (Hutton et al, 2002). The ACE method in Han et al (2004) uses a level set based framework that corrects for buried sulci using fuzzy CSF membership information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Registration based cortical thickness measurement

et al. 2009

View full text Add to dashboard Cite

Cortical thickness is an important biomarker for image-based studies of the brain. A Diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray mattercerebrospinal fluid interface is given by a Diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.

show abstract

“…The voxel-based method of Hutton et al [7] uses the Laplacian definition of thickness and thickness information to identify deep sulci. However, no additional information is used to ensure that sulci are correctly identified.…”

Section: Introductionmentioning

confidence: 99%

Efficient Use of Cerebral Cortical Thickness to Correct Brain MR Segmentation

Diep

Bourgeat

Ourselin

2007

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

View full text Add to dashboard Cite

Efficient, automatic and robust tools for measurement of cerebral cortical thickness would aid diagnosis and longitudinal studies of neurodegenerative disorders. In this work, we segment a 3D magnetic resonance image of the brain using an Expectation-Maximization approach. The definition of thickness used is based on the solution of Laplace's equation in the cortex. Unlike other works, finite difference equations for calculation of cortical thickness are generalized for anisotropic images in order to avoid resampling the input images. We also developed a method which combines information from the thickness estimation with the segmentation probability maps, in order to detect missegmented sulci and correct the segmentation accordingly.

show abstract

Sulcal Segmentation for Cortical Thickness Measurements

Cited by 8 publications

References 10 publications

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps

Registration based cortical thickness measurement

Efficient Use of Cerebral Cortical Thickness to Correct Brain MR Segmentation

Contact Info

Product

Resources

About