7,960 research outputs found
Persistent Homology in Sparse Regression and its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that
determines the sparsity of the system. How to choose the right tuning parameter
is a fundamental and difficult problem in learning the sparse system. In this
paper, by treating the the tuning parameter as an additional dimension,
persistent homological structures over the parameter space is introduced and
explored. The structures are then further exploited in speeding up the
computation using the proposed soft-thresholding technique. The topological
structures are further used as multivariate features in the tensor-based
morphometry (TBM) in characterizing white matter alterations in children who
have experienced severe early life stress and maltreatment. These analyses
reveal that stress-exposed children exhibit more diffuse anatomical
organization across the whole white matter region.Comment: submitted to IEEE Transactions on Medical Imagin
Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia
Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of
brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in
brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest
quantitative differences in brain texture that, alongside discrete volumetric changes, may
serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and
voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27
patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy)
were also used as covariates in VBM analyses to test for correspondence with regional brain
volume. Linear discriminant analysis tested if texture and volumetric data predicted
diagnostic group membership (schizophrenia or control). We found that uniformity and
entropy of grey matter differed significantly between individuals with schizophrenia and
controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group,
these texture parameters correlated with volumes of the left hippocampus, right amygdala
and cerebellum. The best predictor of diagnostic group membership was the combination of
fine texture heterogeneity and left hippocampal size. This study highlights the presence of
distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural
abnormality of the hippocampus. The conjunction of these features has potential as a
neuroimaging endophenotype of schizophrenia
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The role of HG in the analysis of temporal iteration and interaural correlation
Going deeper with brain morphometry using neural networks
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated
biomarker for many neurodegenerative diseases. Recent advances in this domain
indicate that deep convolutional neural networks can infer morphometric
measurements within a few seconds. Nevertheless, the accuracy of the devised
model for insightful bio-markers (mean curvature and thickness) remains
unsatisfactory. In this paper, we propose a more accurate and efficient neural
network model for brain morphometry named HerstonNet. More specifically, we
develop a 3D ResNet-based neural network to learn rich features directly from
MRI, design a multi-scale regression scheme by predicting morphometric measures
at feature maps of different resolutions, and leverage a robust optimization
method to avoid poor quality minima and reduce the prediction variance. As a
result, HerstonNet improves the existing approach by 24.30% in terms of
intraclass correlation coefficient (agreement measure) to FreeSurfer
silver-standards while maintaining a competitive run-time
Feature Extraction
Feature extraction is a procedure aimed at selecting and transforming a data set in order to increase the performance of a pattern recognition or machine learning system. Nowadays, since the amount of data available and its dimension is growing exponentially, it is a fundamental procedure to avoid overfitting and the curse of dimensionality, while, in some cases, allowing a interpretative analysis of the data. The topic itself is a thriving discipline of study, and it is difficult to address every single feature extraction algorithm. Therefore, we provide an overview of the topic, introducing widely used techniques, while at the same time presenting some domain-specific feature extraction algorithms. Finally, as a case, study, we will illustrate the vastness of the field by analysing the usage and impact of feature extraction in neuroimaging
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
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