310 research outputs found
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
The health and function of tissue rely on its vasculature network to provide
reliable blood perfusion. Volumetric imaging approaches, such as multiphoton
microscopy, are able to generate detailed 3D images of blood vessels that could
contribute to our understanding of the role of vascular structure in normal
physiology and in disease mechanisms. The segmentation of vessels, a core image
analysis problem, is a bottleneck that has prevented the systematic comparison
of 3D vascular architecture across experimental populations. We explored the
use of convolutional neural networks to segment 3D vessels within volumetric in
vivo images acquired by multiphoton microscopy. We evaluated different network
architectures and machine learning techniques in the context of this
segmentation problem. We show that our optimized convolutional neural network
architecture, which we call DeepVess, yielded a segmentation accuracy that was
better than both the current state-of-the-art and a trained human annotator,
while also being orders of magnitude faster. To explore the effects of aging
and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of
cortical blood vessels in young and old mouse models of Alzheimer's disease and
wild type littermates. We found little difference in the distribution of
capillary diameter or tortuosity between these groups, but did note a decrease
in the number of longer capillary segments () in aged animals as
compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure
NetMets: software for quantifying and visualizing errors in biological network segmentation
One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization
funcGNN: A Graph Neural Network Approach to Program Similarity
Program similarity is a fundamental concept, central to the solution of
software engineering tasks such as software plagiarism, clone identification,
code refactoring and code search. Accurate similarity estimation between
programs requires an in-depth understanding of their structure, semantics and
flow. A control flow graph (CFG), is a graphical representation of a program
which captures its logical control flow and hence its semantics. A common
approach is to estimate program similarity by analysing CFGs using graph
similarity measures, e.g. graph edit distance (GED). However, graph edit
distance is an NP-hard problem and computationally expensive, making the
application of graph similarity techniques to complex software programs
impractical. This study intends to examine the effectiveness of graph neural
networks to estimate program similarity, by analysing the associated control
flow graphs. We introduce funcGNN, which is a graph neural network trained on
labeled CFG pairs to predict the GED between unseen program pairs by utilizing
an effective embedding vector. To our knowledge, this is the first time graph
neural networks have been applied on labeled CFGs for estimating the similarity
between high-level language programs. Results: We demonstrate the effectiveness
of funcGNN to estimate the GED between programs and our experimental analysis
demonstrates how it achieves a lower error rate (0.00194), with faster (23
times faster than the quickest traditional GED approximation method) and better
scalability compared with the state of the art methods. funcGNN posses the
inductive learning ability to infer program structure and generalise to unseen
programs. The graph embedding of a program proposed by our methodology could be
applied to several related software engineering problems (such as code
plagiarism and clone identification) thus opening multiple research directions.Comment: 11 pages, 8 figures, 3 table
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