Ventricle Boundary in CT: Partial Volume Effect and Local Thresholds

We present a mathematical frame to carry out segmentation of cerebrospinal fluid (CSF) of ventricular region in computed tomography (CT) images in the presence of partial volume effect (PVE). First, the image histogram is fitted using the Gaussian mixture model (GMM). Analyzing the GMM, we find global threshold based on parameters of distributions for CSF, and for the combined white and grey matter (WGM). The parameters of distribution of PVE pixels on the boundary of ventricles are estimated by using a convolution operator. These parameters are used to calculate local thresholds for boundary pixels by the analysis of contribution of the neighbor pixels intensities into a PVE pixel. The method works even in the case of an almost unimodal histogram; it can be useful to analyze the parameters of PVE in the ground truth provided by the expert

Automatic segmentation of magnetic resonance images of the brain

Magnetic resonance imaging (MRI) is a technique used primarily in medical settings to produce high quality images of the human body’s internal anatomy. Each image is of a thin slice through the body, with the typical distance between slices being a few millimeters. Brain segmentation is the delineation of one or more anatomical structures within images of the brain. It promotes greater understanding of spatial relationships to aid in such tasks as surgical planning and clinical diagnoses, particularly when the segmented outlines from each image slice are displayed together as a surface in three-dimensions. A review of the literature indicates that current brain segmentation methods require a trained human expert to inspect the images and decide appropriate parameters, thresholds, or regions of interest to achieve the proper segmentation. This is a tedious time-consuming task because of the large number of images involved. A truly automatic method is needed to transform brain segmentation into a practical clinical tool. This dissertation describes a novel pattern classification approach to the problem of automatically segmenting magnetic resonance images of the brain. Based on this approach, algorithms were designed and implemented to automatically segment a number of anatomical structures. These algorithms were applied to several standard image data sets of human subjects obtained from the Internet Brain Segmentation Repository (IBSR). The resulting segmentations of the lateral ventricles and the caudate nuclei were compared to reference manual segmentations done by expert radiologists. The Tanimoto similarity coefficient was very good for the lateral ventricles (0.81) and good for the caudate nuclei (0.67)

Observation of Angular Dependence of T1 in the Human White Matter at 3T

Background and Objective: Multiple factors including chemical composition and microstructure influence relaxivity of tissue water in vivo. We have quantified T1 in the human white mater (WM) together with diffusion tensor imaging to study a possible relationship between water T1, diffusional fractional anisotropy (FA) and fibre-to-field angle. Methods: An inversion recovery (IR) pulse sequence with 6 inversion times for T1 and a multi-band diffusion tensor sequence with 60 diffusion sensitizing gradient directions for FA and the fibre-to-field angle θ (between the principal direction of diffusion and B0) were used at 3 Tesla in 40 healthy subjects. T1 was assessed using the method previously applied to anisotropy of coherence lifetime to provide a heuristic demonstration as a surface plot of T1 as a function of FA and the angle θ. Results: Our data show that in the WM voxels with FA > 0.3 T1 becomes longer (i.e. 1/T1 = R1 slower) when fibre-to-field angle is 50–60°, approximating the magic angle of 54.7°. The longer T1 around the magic angle was found in a number of WM tracts independent of anatomy. S0 signal intensity, computed from IR fits, mirrored that of T1 being greater in the WM voxels when the fibre-to-field angle was 50–60°. Conclusions: The current data point to fibre-to-field-angle dependent T1 relaxation in WM as an indication of effects of microstructure on the longitudinal relaxation of water

Automated morphometric analysis and phenotyping of mouse brains from structural µMR images

In light of the utility and increasing ubiquity of mouse models of genetic and neurological disease, I describefully automated pipelines for the investigation of structural microscopic magnetic resonance images of mouse brains – for both high-throughput phenotyping, and monitoring disease. Mouse models offer unparalleled insight into genetic function and brain plasticity, in phenotyping studies; and neurodegenerative disease onset and progression, in therapeutic trials. I developed two cohesive, automatic software tools, for Voxel- and Tensor-Based Morphometry (V/TBM) and the Boundary Shift Integral (BSI), in the mouse brain. V/TBM are advantageous for their ability to highlight morphological differences between groups, without laboriously delineating regions of interest. The BSI is a powerful and sensitive imaging biomarker for the detection of atrophy. The resulting pipelines are described in detail. I show the translation and application of open-source software developed for clinical MRI analysis to mouse brain data: for tissue segmentation into high-quality, subject-specific maps, using contemporary multi-atlas techniques; and for symmetric, inverse-consistent registration. I describe atlases and parameters suitable for the preclinical paradigm, and illustrate and discuss image processing challenges encountered and overcome during development. As proof of principle and to illustrate robustness, I used both pipelines with in and ex vivo mouse brain datasets to identify differences between groups, representing the morphological influence of genes, and subtle, longitudinal changes over time, in particular relation to Down syndrome and Alzheimer’s disease. I also discuss the merits of transitioning preclinical analysis from predominately ex vivo MRI to in vivo, where morphometry is still viable and fewer mice are necessary. This thesis conveys the cross-disciplinary translation of up-to-date image analysis techniques to the preclinical paradigm; the development of novel methods and adaptations to robustly process large cohorts of data; and the sensitive detection of phenotypic differences and neurodegenerative changes in the mouse brai

Validating Diffusion Spectrum Imaging-Based Fiber Tractography for Cognitive Neuroscience Research

White matter fiber tractography based on diffusion-weighted magnetic resonance imaging is a promising method for non-invasive investigation of anatomical connectivity in the human brain. Knowledge of the white matter connections linking functional brain areas can inform interpretation of functional imaging results and allow the construction of biologically informed computational and statistical models. However, relatively little attention has been paid to the reproducibility and external validity of tractography results, even as the user base of this technology continues to grow, and as tractography research is applied to cognitive neuroscience research in novel ways. In this investigation, we addressed the reliability and validity of deterministic tractography results based on diffusion spectrum imaging (DSI). Reliability was evaluated both in terms of the presence/absence of fiber connections across sessions and the correlation of fiber density values. Validity was assessed by comparing tractography results to findings from invasive studies of the macaque monkey: we focused on the cortical and subcortical connections of the frontal eye fields (FEF). Results indicated significant variability in tractography: on average, intercortical connections present in one session had only a 75% likelihood of being detected in a second session from the same individual. However, the fiber density of repeatedly-detected connections was highly reliable, with an average between-session correlation coefficient of 0.94. Next, we investigated how global vs. targeted tractography approaches affected reliability and detection power. We found that a targeted approach, involving the use of region-of interest (ROI) constraints, yielded a large advantage in detection power and modest improvements in reliability. Finally, fiber connections of the human FEF were broadly consistent with hypotheses derived from a meta-analysis of macaque findings: we found reliable projections to the supplementary eye fields (SEF), striatum, thalamus, and parietal cortex. In contrast, we found lesser connectivity to a set of foil regions. The combined results of this study validate the use of DSI-based fiber tractography to address hypotheses relating to human brain connectivity. However, widespread noise in tractography results highlights the need for conservative approaches to fiber tracking research. We especially emphasize the benefits of collecting multiple data samples per participant and of addressing targeted hypotheses

Active Shape Model Segmentation of Brain Structures in MR Images of Subjects with Fetal Alcohol Spectrum Disorder

Fetal Alcohol Spectrum Disorder (FASD) is the most common form of preventable mental retardation worldwide. This condition affects children whose mothers excessively consume alcohol whilst pregnant. FASD can be identied by physical and mental defects, such as stunted growth, facial deformities, cognitive impairment, and behavioural abnormalities. Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of FASD. One such approach aims to detect brain abnormalities through an assessment of volume and shape of sub-cortical structures on high-resolution MR images. Two brain structures of interest are the Caudate Nucleus and Hippocampus. Manual segmentation of these structures is time-consuming and subjective. We therefore present a method for automatically segmenting the Caudate Nucleus and Hippocampus from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model (ASM), which is used to learn shape variation from manually segmented training data. A discrete Geometrically Deformable Model (GDM) is rst deformed to t the relevant structure in each training set. The vertices belonging to each GDM are then used as 3D landmark points - effectively generating point correspondence between training models. An ASM is then created from the landmark points. This ASM is only able to deform to t structures with similar shape to those found in the training data. There are many variations of the standard ASM technique - each suited to the segmentation of data with particular characteristics. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the research data. Various popular image search techniques were tested, including an edge detection method and a method based on grey prole Mahalanobis distance measurement. A heuristic image search method, especially designed to target Caudate Nuclei and Hippocampi, was also developed and tested. This method was extended to include multisampling of voxel proles. ASM segmentation quality was evaluated according to various quantitative metrics, including: overlap, false positives, false negatives, mean squared distance and Hausdorff distance. Results show that ASMs that use the heuristic image search technique, without multisampling, produce the most accurate segmentations. Mean overlap for segmentation of the various target structures ranged from 0.76 to 0.82. Mean squared distance ranged from 0.72 to 0.76 - indicating sub-1mm accuracy, on average. Mean Hausdorff distance ranged from 2:7mm to 3:1mm. An ASM constructed using our heuristic technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study - thereby facilitating a better understanding of the eects of this unfortunate condition

Segmentation of brain MRI structures with deep machine learning

Several studies on brain Magnetic Resonance Images (MRI) show relations between neuroanatomical abnormalities of brain structures and neurological disorders, such as Attention De fficit Hyperactivity Disorder (ADHD) and Alzheimer. These abnormalities seem to be correlated with the size and shape of these structures, and there is an active fi eld of research trying to find accurate methods for automatic MRI segmentation. In this project, we study the automatic segmentation of structures from the Basal Ganglia and we propose a new methodology based on Stacked Sparse Autoencoders (SSAE). SSAE is a strategy that belongs to the family of Deep Machine Learning and consists on a supervised learning method based on an unsupervisely pretrained Feed-forward Neural Network. Moreover, we present two approaches based on 2D and 3D features of the images. We compare the results obtained on the di fferent regions of interest with those achieved by other machine learning techniques such as Neural Networks and Support Vector Machines. We observed that in most cases SSAE improves those other methods. We demonstrate that the 3D features do not report better results than the 2D ones as could be thought. Furthermore, we show that SSAE provides state-of-the-art Dice Coe fficient results (left, right): Caudate (90.6+-3 1.4, 90.31 +-1.7), Putamen (91.03 +-1.4, 90.82+- 1.4), Pallidus (85.11+-1.8, 83.47 +-2.2), Accumbens (74.26+- 4.4, 74.46 +-4.6)

A four-dimensional probabilistic atlas of the human brain

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype-phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders

Automatic quantification of brain midline shift in CT images

Ph.DDOCTOR OF PHILOSOPH