LoAd: a locally adaptive cortical segmentation algorithm

Hippocampal atrophy in Alzheimer’s disease

SUMMARY New research criteria for Alzheimer’s disease (AD) and mild cognitive impairment (MCI) emphasize the use of imaging biomarkers in clinical diagnosis of these disorders. The volume loss of medial temporal lobe structures, especially hippocampal atrophy, is the best validated marker of AD. Manual tracing on MRI is the present gold standard for evaluating hippocampal volume; however, it is laborious and tracer-dependent. We categorized the most recent full- or semi-automated methods by the nature of the output of the method: size and shape of subcortical structures, cortical thickness, atrophy-rate and voxel- and region-based characteristics. The features of each method are introduced. The findings in structural MRI studies, especially in those studies utilizing the most recent methods, and the accuracies of those new methods in differentiating AD from healthy controls and stable MCI from progressive MCI are reviewed.

Cortical folding analysis on patients with Alzheimer's disease and mild cognitive impairment

Cortical thinning is a widely used and powerful biomarker for measuring disease progression in Alzheimer's disease (AD). However, there has been little work on the effect of atrophy on the cortical folding patterns. In this study, we examined whether the cortical folding could be used as a biomarker of AD. Cortical folding metrics were computed on 678 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. For each subject, the boundary between grey matter and white matter was extracted using a level set technique. At each point on the boundary two metrics characterising folding, curvedness and shape index, were generated. Joint histograms using these metrics were calculated for five regions of interest (ROIs): frontal, temporal, occipital, and parietal lobes as well as the cingulum. Pixelwise statistical maps were generated from the joint histograms using permutations tests. In each ROI, a significant reduction was observed between controls and AD in areas associated with the sulcal folds, suggesting a sulcal opening associated with neurodegeneration. When comparing to MCI patients, the regions of significance were smaller but overlapping with those regions found comparing controls to AD. It indicates that the differences in cortical folding are progressive and can be detected before formal diagnosis of AD. Our preliminary analysis showed a viable signal in the cortical folding patterns for Alzheimer's disease that should be explored further.

Cortical surface mapping using topology correction, partial flattening and 3D shape context-based non-rigid registration for use in quantifying atrophy in Alzheimer's disease

Magnetic resonance (MR) provides a non-invasive way to investigate changes in the brain resulting from aging or neurodegenerative disorders such as Alzheimer's disease (AD). Performing accurate analysis for population studies is challenging because of the interindividual anatomical variability. A large set of tools is found to perform studies of brain anatomy and population analysis (FreeSurfer, SPM, FSL). In this paper we present a newly developed surface-based processing pipeline (MILXCTE) that allows accurate vertex-wise statistical comparisons of brain modifications, such as cortical thickness (CTE). The brain is first segmented into the three main tissues: white matter, gray matter and cerebrospinal fluid, after CTE is computed, a topology corrected mesh is generated. Partial inflation and non-rigid registration of cortical surfaces to a common space using shape context are then performed. Each of the steps was firstly validated using MR images from the OASIS database. We then applied the pipeline to a sample of individuals randomly selected from the AIBL study on AD and compared with FreeSurfer. For a population of 50 individuals we found correlation of cortical thickness in all the regions of the brain (average r=0.62 left and r=0.64 right hemispheres). We finally computed changes in atrophy in 32 AD patients and 81 healthy elderly individuals. Significant differences were found in regions known to be affected in AD. We demonstrated the validity of the method for use in clinical studies which provides an alternative to well established techniques to compare different imaging biomarkers for the study of neurodegenerative diseases.

Adaptive neonate brain segmentation

Babies born prematurely are at increased risk of adverse neurodevelopmental outcomes. Recent advances suggest that measurement of brain volumes can help in defining biomarkers for neurodevelopmental outcome. These techniques rely on an accurate segmentation of the MRI data. However, due to lack of contrast, partial volume (PV) effect, the existence of both hypo- and hyper-intensities and significant natural and pathological anatomical variability, the segmentation of neonatal brain MRI is challenging. We propose a pipeline for image segmentation that uses a novel multi-model Maximum a posteriori Expectation Maximisation (MAP-EM) segmentation algorithm with a prior over both intensities and the tissue proportions, a B0 inhomogeneity correction, and a spatial homogeneity term through the use of a Markov Random Field. This robust and adaptive technique enables the segmentation of images with high anatomical disparity from a normal population. Furthermore, the proposed method implicitly models Partial Volume, mitigating the problem of neonatal white/grey matter intensity inversion. Experiments performed on a clinical cohort show expected statistically significant correlations with gestational age at birth and birthweight. Furthermore, the proposed method obtains statistically significant improvements in Dice scores when compared to the a Maximum Likelihood EM algorithm.

A comparison of voxel and surface based cortical thickness estimation methods

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.