Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

Segmentation of brain MRI during early childhood

The objective of this thesis is the development of automatic methods to measure the changes in volume and growth of brain structures in prematurely born infants. Automatic tools for accurate tissue quantification from magnetic resonance images can provide means for understanding how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or behavioural impairment, are related to underlying changes in brain anatomy. Understanding these changes forms a basis for development of suitable treatments to improve the outcomes of premature birth. In this thesis we focus on the segmentation of brain structures from magnetic resonance images during early childhood. Most of the current brain segmentation techniques have been focused on the segmentation of adult or neonatal brains. As a result of rapid development, the brain anatomy during early childhood differs from anatomy of both adult and neonatal brains and therefore requires adaptations of available techniques to produce good results. To address the issue of anatomical differences of the brain during early childhood compared to other age-groups, population-specific deformable and probabilistic atlases are introduced. A method for generation of population-specific prior information in form of a probabilistic atlas is proposed and used to enhance existing segmentation algorithms. The evaluation of registration-based and intensity-based approaches shows the techniques to be complementary in the quality of automatic segmentation in different parts of the brain. We propose a novel robust segmentation method combining the advantages of both approaches. The method is based on multiple label propagation using B-spline non-rigid registration followed by EM segmentation. Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which significantly affects modern high resolution MR data acquired at higher magnetic field strengths. A novel template based method focused on correcting the intensity inhomogeneity in data acquired at higher magnetic field strengths is therefore proposed. The proposed segmentation method combined with proposed intensity inhomogeneity correction method offers a robust tool for quantification of volumes and growth of brain structures during early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age

Atlas-Guided Segmentation of Vervet Monkey Brain MRI

The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model

Segmentation of anatomical structures in brain MR images using atlases in FSL - a quantitative approach

Segmentation of brain structures from MR images is crucial in understanding the disease progress, diagnosis, and treatment monitoring. Atlases, showing the ex- pected locations of the structures, are commonly used to start and guide the segmentation process. In many cases, the quality of the atlas may have a significant effect in the final result. In the literature, commonly used atlases may be obtained from one subject’s data, only from the healthy, or depict only certain structures that limit their accuracy. Anatomical variations, pathologies, imaging artifacts all could aggravate the problems related to application of atlases. In this paper, we propose to use multiple atlases that are sufficiently different from each other as much as possible to handle such problems. To this effect, we have built a library of atlases and computed their similarity values to each other. Our study showed that the existing atlases have varying levels of similarity for different structures

Using neurite orientation dispersion and density imaging and tracts constrained by underlying anatomy to differentiate between subjects along the Alzheimer's disease continuum

OBJECTIVE: To assess the involvement of the white matter of the brain in the pathology of Alzheimer’s disease. Using Neurite Orientation Density and Dispersion Imaging (NODDI) and the probabilistic white matter parcellation tool Tracula as a means for understanding whether alterations in the white matter underlie changes in perceived cognitive abilities across the spectrum from health aging to Alzheimer’s disease. METHOD: Data were obtained from 28 participants in the Health Outreach Program for the Elderly (HOPE) at the Boston University Alzheimer’s Disease Center (BU ADC) Clinical Core Registry. MRI scans included an MPRAGE T1 scan, multi-b shell diffusion scan and a High Angular Resolution Diffusion Imaging scan (HARDI). Scans were processed with Freesurfer v6.0 and the NODDI Python2.7 toolkit. The resulting data included the orientation dispersion index (ODI) and Fractional Anisotropy (FA) values for cortical and subcortical regions in the DKT atlas space as well as specific Tracts Constrained by Underlying Anatomy (TRACULA) measurements for 18 specific established white matter tracts. Statistical models using measures of pathway integrity (FA and ODI data) were used to assess relationships with Informant Cognitive Change Index (ICCI), self-described Cognitive Change Index (CCI), and Clinical Dementia Rating (CDR) values. RESULTS: Measures of white matter integrity within several tracts predicted ICCI and CDR well in statistical models. FA and ODI values of the bilateral superior longitudinal fasciculi, inferior longitudinal fasciculi, and the cingulum bundle tracts were all related to ICCI and CDR. None of the known tracts’ FA or ODI values were related to CCI. CONCLUSIONS: Measures of white matter pathway integrity were predictive of ICCI and CDR scores but not CCI. These finding support the notion that self-report of cognitive abilities may be compromised by alterations in insight and reinforce the need for informed study partners and clinical ratings to evaluate potential MCI and AD

Assessment of reliability of multi-site neuroimaging via traveling phantom study

pre-printThis paper describes a framework for quantitative analysis of neuroimaging data of traveling human phantoms used for cross-site validation. We focus on the analysis of magnetic resonance image data including intra- and intersite comparison. Locations and magnitude of geometric deformation is studied via unbiased atlas building and metrics on deformation fields. Variability of tissue segmentation is analyzed by comparison of volumes, overlap of tissue maps, and a new Kullback-Leibler divergence on tissue probabilities, with emphasis on comparing probabilistic rather than binary segmentations. We show that results from this information theoretic measure are highly correlated with overlap. Reproducibility of automatic, atlas-based segmentation of subcortical structures is examined by comparison of volumes, shape overlap and surface distances. Variability among scanners of the same type but also differences to a different scanner type are discussed. The results demonstrate excellent reliability across multiple sites that can be achieved by the use of the today's scanner generation and powerful automatic analysis software. Knowledge about such variability is crucial for study design and power analysis in new multi-site clinical studies. Keywords: Multi-site neuroimaging study, validation, traveling phantom, automatic segmentation, cross-site validation