31,327 research outputs found
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
Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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