6,778 research outputs found
Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
With the introduction of spectral-domain optical coherence tomography (OCT),
resulting in a significant increase in acquisition speed, the fast and accurate
segmentation of 3-D OCT scans has become evermore important. This paper
presents a novel probabilistic approach, that models the appearance of retinal
layers as well as the global shape variations of layer boundaries. Given an OCT
scan, the full posterior distribution over segmentations is approximately
inferred using a variational method enabling efficient probabilistic inference
in terms of computationally tractable model components: Segmenting a full 3-D
volume takes around a minute. Accurate segmentations demonstrate the benefit of
using global shape regularization: We segmented 35 fovea-centered 3-D volumes
with an average unsigned error of 2.46 0.22 {\mu}m as well as 80 normal
and 66 glaucomatous 2-D circular scans with errors of 2.92 0.53 {\mu}m
and 4.09 0.98 {\mu}m respectively. Furthermore, we utilized the inferred
posterior distribution to rate the quality of the segmentation, point out
potentially erroneous regions and discriminate normal from pathological scans.
No pre- or postprocessing was required and we used the same set of parameters
for all data sets, underlining the robustness and out-of-the-box nature of our
approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie
Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey
© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease
Modelling Neuron Morphology: Automated Reconstruction from Microscopy Images
Understanding how the brain works is, beyond a shadow of doubt, one of the greatest challenges for modern science. Achieving a deep knowledge about the structure, function and development of the nervous system at the molecular, cellular and network levels is crucial in this attempt, as processes at all these scales are intrinsically linked with higher-order cognitive functions. The research in the various areas of neuroscience deals with advanced imaging techniques, collecting an increasing amounts of heterogeneous and complex data at different scales. Then, computational tools and neuroinformatics solutions are required in order to integrate and analyze the massive quantity of acquired information.
Within this context, the development of automaticmethods and tools for the study of neuronal anatomy has a central role. The morphological properties of the soma and of the axonal and dendritic arborizations constitute a key discriminant for the neuronal phenotype and play a determinant role in network connectivity. A quantitative analysis allows the study of possible factors influencing neuronal development, the neuropathological abnormalities related to specific syndromes, the relationships
between neuronal shape and function, the signal transmission and the network connectivity. Therefore, three-dimensional digital reconstructions of soma, axons and dendrites are indispensable for exploring neural networks. This thesis proposes a novel and completely automatic pipeline for neuron reconstruction with operations ranging from the detection and segmentation of the soma to the dendritic arborization tracing. The pipeline can deal with different datasets and acquisitions both at the network and at the single scale level without any user interventions or manual adjustment. We developed an ad hoc approach for the localization and segmentation of neuron bodies. Then, various methods and research lines have been investigated for the reconstruction of the whole dendritic arborization of each neuron, which is solved both in 2D and in 3D images
Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis
The aim of this thesis is to develop automated methods for the analysis of the
spatial patterns, and the functional behaviour of endothelial cells, viewed under
microscopy, with applications to the understanding of atherosclerosis.
Initially, a radial search approach to segmentation was attempted in order to
trace the cell and nuclei boundaries using a maximum likelihood algorithm; it
was found inadequate to detect the weak cell boundaries present in the available
data. A parametric cell shape model was then introduced to fit an equivalent
ellipse to the cell boundary by matching phase-invariant orientation fields of the
image and a candidate cell shape. This approach succeeded on good quality
images, but failed on images with weak cell boundaries. Finally, a support
vector machines based method, relying on a rich set of visual features, and a
small but high quality training dataset, was found to work well on large numbers
of cells even in the presence of strong intensity variations and imaging noise.
Using the segmentation results, several standard shear-stress dependent parameters
of cell morphology were studied, and evidence for similar behaviour
in some cell shape parameters was obtained in in-vivo cells and their nuclei.
Nuclear and cell orientations around immature and mature aortas were broadly
similar, suggesting that the pattern of flow direction near the wall stayed approximately
constant with age. The relation was less strong for the cell and
nuclear length-to-width ratios.
Two novel shape analysis approaches were attempted to find other properties
of cell shape which could be used to annotate or characterise patterns, since a
wide variability in cell and nuclear shapes was observed which did not appear
to fit the standard parameterisations. Although no firm conclusions can yet be
drawn, the work lays the foundation for future studies of cell morphology.
To draw inferences about patterns in the functional response of cells to flow,
which may play a role in the progression of disease, single-cell analysis was performed
using calcium sensitive florescence probes. Calcium transient rates were
found to change with flow, but more importantly, local patterns of synchronisation
in multi-cellular groups were discernable and appear to change with flow.
The patterns suggest a new functional mechanism in flow-mediation of cell-cell
calcium signalling
Computational processing and analysis of ear images
Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201
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