2,217 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates
Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55 approximately 90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18 approximately 96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5 approximately 18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.published_or_final_versio
Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.
The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed
Quantitation in MRI : application to ageing and epilepsy
Multi-atlas propagation and label fusion techniques have recently been developed for segmenting
the human brain into multiple anatomical regions. In this thesis, I investigate
possible adaptations of these current state-of-the-art methods. The aim is to study ageing
on the one hand, and on the other hand temporal lobe epilepsy as an example for a
neurological disease.
Overall effects are a confounding factor in such anatomical analyses. Intracranial volume
(ICV) is often preferred to normalize for global effects as it allows to normalize for estimated
maximum brain size and is hence independent of global brain volume loss, as seen
in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T
versus 3T, and present an automated method of measuring intracranial volume, Reverse
MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I
show that this is comparable to manual measurements and robust against field strength
differences.
Correct and robust segmentation of target brains which show gross abnormalities, such as
ventriculomegaly, is important for the study of ageing and disease. We achieved this with
incorporating tissue classification information into the image registration process. The
best results in elderly subjects, patients with TLE and healthy controls were achieved using
a new approach using multi-atlas propagation with enhanced registration (MAPER).
I then applied MAPER to the problem of automatically distinguishing patients with TLE
with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and
determine the side of seizure onset. MAPER-derived structural volumes were used for
a classification step consisting of selecting a set of discriminatory structures and applying
support vector machine on the structural volumes as well as morphological similarity
information such as volume difference obtained with spectral analysis. Acccuracies were
91-100 %, indicating that the method might be clinically useful.
Finally, I used the methods developed in the previous chapters to investigate brain regional
volume changes across the human lifespan in over 500 healthy subjects between 20
to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI
database. We were able to confirm several known changes, indicating the veracity of the
method. In addition, we describe the first multi-region, whole-brain database of normal
ageing
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