2,219 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
Genus Computing for 3D digital objects: algorithm and implementation
This paper deals with computing topological invariants such as connected
components, boundary surface genus, and homology groups. For each input data
set, we have designed or implemented algorithms to calculate connected
components, boundary surfaces and their genus, and homology groups. Due to the
fact that genus calculation dominates the entire task for 3D object in 3D
space, in this paper, we mainly discuss the calculation of the genus. The new
algorithms designed in this paper will perform:
(1) pathological cases detection and deletion, (2) raster space to point
space (dual space) transformation, (3) the linear time algorithm for boundary
point classification, and (4) genus calculation.Comment: 12 pages 7 figures. In Proceedings of the Workshop on Computational
Topology in image context 2009, Aug. 26-28, Austria, Edited by W. Kropatsch,
H. M. Abril and A. Ion, 200
Décomposition volumique d'images pour l'étude de la microstructure de la neige
Les avalanches de neige sont des phénomènes naturels complexes dont l'occurrence s'explique principalement par la structure et les propriétés du manteau neigeux. Afin de mieux comprendre les évolutions de ces propriétés au cours du temps, il est important de pouvoir caractériser la microstructure de la neige, notamment en termes de grains et de ponts de glace les reliant. Dans ce contexte, l'objectif de cette thèse est la décomposition d'échantillons de neige en grains individuels à partir d'images 3-D de neige obtenues par microtomographie X. Nous présentons ici deux méthodes de décomposition utilisant des algorithmes de géométrie discrète. Sur la base des résultats de ces segmentations, certains paramètres, comme la surface spécifique et la surface spécifique de contact entre grains sont ensuite estimés sur des échantillons de neiges variées. Ces méthodes de segmentation ouvrent de nouvelles perspectives pour la caractérisation de la microstructure de la neige, de ses propriétés, ainsi que de leur évolution au cours du temps.Snow avalanches are complex natural phenomena whose occurrence is mainly due to the structure and properties of the snowpack. To better understand the evolution of these properties over time, it is important to characterize the microstructure of snow, especially in terms of grains and ice necks that connect them. In this context, the objective of this thesis is the decomposition of snow samples into individual grains from 3-D images of snow obtained by X-ray microtomography. We present two decomposition methods using algorithms of discrete geometry. Based on the results of these segmentations, some parameters such as the specific surface area and the specific contact area between grains are then estimated from samples of several snow types. These segmentation methods offer new outlooks for the characterization of the microstructure of snow, its properties, and its time evolution
Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics
Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with cell differentiation, development, proliferation, and disease. Changes in the nuclear form are associated with reorganization of chromatin architecture related to altered functional properties such as gene regulation and expression. Understanding these processes through quantitative analysis of morphological changes is important not only for investigating nuclear organization, but also has clinical implications, for example, in detection and treatment of pathological conditions such as cancer.
While efforts have been made to characterize nuclear shapes in two or pseudo-three dimensions, several studies have demonstrated that three dimensional (3D) representations provide better nuclear shape description, in part due to the high variability of nuclear morphologies. 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation. A few methods have been proposed to classify nuclear morphologies in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. There is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analyses.
In this work, we address a number of these existing limitations.
First, we present a largest publicly available, to-date, 3D microscopy imaging dataset for cell nuclear morphology analysis and classification. We provide a detailed description of the image analysis protocol, from segmentation to baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. We proposed a specific cross-validation scheme that accounts for possible batch effects in data.
Second, we propose a new technique that combines mathematical modeling, machine learning, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. Employing robust and smooth surface reconstruction methods to accurately approximate 3D object boundary enables the establishment of homologies between different biological shapes. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. We combine these methods into a highly parallel computational pipeline workflow for automated morphological analysis of thousands of nuclei and nucleoli in 3D. We also describe the use of visual analytics and deep learning techniques for the analysis of nuclear morphology data.
Third, we evaluate proposed methods for 3D surface morphometric analysis of our data.
We improved the performance of morphological classification between epithelial vs mesenchymal human prostate cancer cells compared to the previously reported results due to the more accurate shape representation and the use of combined nuclear and nucleolar morphometry. We confirmed previously reported relevant morphological characteristics, and also reported new features that can provide insight in the underlying biological mechanisms of pathology of prostate cancer. We also assessed nuclear morphology changes associated with chromatin remodeling in drug-induced cellular reprogramming. We computed temporal trajectories reflecting morphological differences in astroglial cell sub-populations administered with 2 different treatments vs controls. We described specific changes in nuclear morphology that are characteristic of chromatin re-organization under each treatment, which previously has been only tentatively hypothesized in literature. Our approach demonstrated high classification performance on each of 3 different cell lines and reported the most salient morphometric characteristics.
We conclude with the discussion of the potential impact of method development in nuclear morphology analysis on clinical decision-making and fundamental investigation of 3D nuclear architecture. We consider some open problems and future trends in this field.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147598/1/akalinin_1.pd
Characterising population variability in brain structure through models of whole-brain structural connectivity
Models of whole-brain connectivity are valuable for understanding neurological function. This thesis
seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically
acquired diffusion data. We propose new approaches for studying these models. The aim is to
develop techniques which can take models of brain connectivity and use them to identify biomarkers
or phenotypes of disease.
The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified
to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections
are traced between 77 regions of interest, automatically extracted by label propagation from
multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract
are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data.
These are compared in subsequent studies.
To date, most whole-brain connectivity studies have characterised population differences using graph
theory techniques. However these can be limited in their ability to pinpoint the locations of differences
in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include
a spectral clustering approach for comparing population differences in the clustering properties of
weighted brain networks. In addition, machine learning approaches are suggested for the first time.
These are particularly advantageous as they allow classification of subjects and extraction of features
which best represent the differences between groups.
One limitation of the proposed approach is that errors propagate from segmentation and registration
steps prior to tractography. This can cumulate in the assignment of false positive connections, where
the contribution of these factors may vary across populations, causing the appearance of population
differences where there are none. The final contribution of this thesis is therefore to develop a common
co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject
into a single probabilistic model of diffusion for the population. This allows tractography to be
performed only once, ensuring that there is one model of connectivity. Cross-subject differences can
then be identified by mapping individual subjects’ anisotropy data to this model. The approach is
used to compare populations separated by age and gender
Minkowski Tensors of Anisotropic Spatial Structure
This article describes the theoretical foundation of and explicit algorithms
for a novel approach to morphology and anisotropy analysis of complex spatial
structure using tensor-valued Minkowski functionals, the so-called Minkowski
tensors. Minkowski tensors are generalisations of the well-known scalar
Minkowski functionals and are explicitly sensitive to anisotropic aspects of
morphology, relevant for example for elastic moduli or permeability of
microstructured materials. Here we derive explicit linear-time algorithms to
compute these tensorial measures for three-dimensional shapes. These apply to
representations of any object that can be represented by a triangulation of its
bounding surface; their application is illustrated for the polyhedral Voronoi
cellular complexes of jammed sphere configurations, and for triangulations of a
biopolymer fibre network obtained by confocal microscopy. The article further
bridges the substantial notational and conceptual gap between the different but
equivalent approaches to scalar or tensorial Minkowski functionals in
mathematics and in physics, hence making the mathematical measure theoretic
method more readily accessible for future application in the physical sciences
Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D
Signal transduction and cell function are governed by the spatiotemporal
organization of membrane-associated molecules. Despite significant advances in
visualizing molecular distributions by 3D light microscopy, cell biologists
still have limited quantitative understanding of the processes implicated in
the regulation of molecular signals at the whole cell scale. In particular,
complex and transient cell surface morphologies challenge the complete sampling
of cell geometry, membrane-associated molecular concentration and activity and
the computing of meaningful parameters such as the cofluctuation between
morphology and signals. Here, we introduce u-Unwrap3D, a framework to remap
arbitrarily complex 3D cell surfaces and membrane-associated signals into
equivalent lower dimensional representations. The mappings are bidirectional,
allowing the application of image processing operations in the data
representation best suited for the task and to subsequently present the results
in any of the other representations, including the original 3D cell surface.
Leveraging this surface-guided computing paradigm, we track segmented surface
motifs in 2D to quantify the recruitment of Septin polymers by blebbing events;
we quantify actin enrichment in peripheral ruffles; and we measure the speed of
ruffle movement along topographically complex cell surfaces. Thus, u-Unwrap3D
provides access to spatiotemporal analyses of cell biological parameters on
unconstrained 3D surface geometries and signals.Comment: 49 pages, 10 figure
Direct occlusion handling for high level image processing algorithms
Many high-level computer vision algorithms suffer in the presence of occlusions caused by multiple objects overlapping in a view. Occlusions remove the direct correspondence between visible areas of objects and the objects themselves by introducing ambiguity in the interpretation of the shape of the occluded object. Ignoring this ambiguity allows the perceived geometry of overlapping objects to be deformed or even fractured. Supplementing the raw image data with a vectorized structural representation which predicts object completions could stabilize high-level algorithms which currently disregard occlusions. Studies in the neuroscience community indicate that the feature points located at the intersection of junctions may be used by the human visual system to produce these completions. Geiger, Pao, and Rubin have successfully used these features in a purely rasterized setting to complete objects in a fashion similar to what is demonstrated by human perception. This work proposes using these features in a vectorized approach to solving the mid-level computer vision problem of object stitching. A system has been implemented which is able extract L and T-junctions directly from the edges of an image using scale-space and robust statistical techniques. The system is sensitive enough to be able to isolate the corners on polygons with 24 sides or more, provided sufficient image resolution is available. Areas of promising development have been identified and several directions for further research are proposed
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