78 research outputs found

    Reconstruction of the neuromuscular junction connectome

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    Motivation: Unraveling the structure and behavior of the brain and central nervous system (CNS) has always been a major goal of neuroscience. Understanding the wiring diagrams of the neuromuscular junction connectomes (full connectivity of nervous system neuronal components) is a starting point for this, as it helps in the study of the organizational and developmental properties of the mammalian CNS. The phenomenon of synapse elimination during developmental stages of the neuronal circuitry is such an example. Due to the organizational specificity of the axons in the connectomes, it becomes important to label and extract individual axons for morphological analysis. Features such as axonal trajectories, their branching patterns, geometric information, the spatial relations of groups of axons, etc. are of great interests for neurobiologists in the study of wiring diagrams. However, due to the complexity of spatial structure of the axons, automatically tracking and reconstructing them from microscopy images in 3D is an unresolved problem. In this article, AxonTracker-3D, an interactive 3D axon tracking and labeling tool is built to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. The ease of use along with accuracy of results makes AxonTracker-3D an attractive tool to obtain valuable quantitative information from axon datasets

    Doctor of Philosophy

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    dissertationConfocal microscopy has become a popular imaging technique in biology research in recent years. It is often used to study three-dimensional (3D) structures of biological samples. Confocal data are commonly multichannel, with each channel resulting from a different fluorescent staining. This technique also results in finely detailed structures in 3D, such as neuron fibers. Despite the plethora of volume rendering techniques that have been available for many years, there is a demand from biologists for a flexible tool that allows interactive visualization and analysis of multichannel confocal data. Together with biologists, we have designed and developed FluoRender. It incorporates volume rendering techniques such as a two-dimensional (2D) transfer function and multichannel intermixing. Rendering results can be enhanced through tone-mappings and overlays. To facilitate analyses of confocal data, FluoRender provides interactive operations for extracting complex structures. Furthermore, we developed the Synthetic Brainbow technique, which takes advantage of the asynchronous behavior in Graphics Processing Unit (GPU) framebuffer loops and generates random colorizations for different structures in single-channel confocal data. The results from our Synthetic Brainbows, when applied to a sequence of developing cells, can then be used for tracking the movements of these cells. Finally, we present an application of FluoRender in the workflow of constructing anatomical atlases

    Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities

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    The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies. Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes. Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics. Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system

    AUTOMATED ANALYSIS OF NEURONAL MORPHOLOGY: DETECTION, MODELING AND RECONSTRUCTION

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    Ph.DDOCTOR OF PHILOSOPH

    Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

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    The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results

    Tracing biofilaments from images : analysis of existing methods to quantify the three-dimensional growth of filamentous fungi on solid substrates

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    Orientador: Prof. Dr. David Alexander MitchellCoorientadora: Prof. Dr. Maura Harumi Sugai-GuériosDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Química. Defesa : Curitiba, 29/05/2018Inclui referências: p.100-113Área de concentração: Engenharia QuímicaResumo: Análise de imagens de biofilamentos tem se tornado uma parte importante na pesquisa em biologia e biotecnologia, pois ela não só elucida a morfologia destas estruturas, mas também fornece ideias sobre o desenvolvimento destas estruturas. Além disso, a morfologia pode ser correlacionada com outras variáveis. Por exemplo, análise de imagem de fungos filamentosos permite correlacionar produtividade de enzimas com diferentes morfologias. Há um interesse em compreender como o micélio de um fungo filamentoso se desevolve ao crescer em substratos sólidos. Para estudar isso, foram obtidas imagens 3D do fungo em crescimento em diversos tempos, objetivando computar dados da morfologia e dinâmica de crescimento: velocidades de extensão da colônia e de pontas, número e posição de ramificações e pontas, comprimentos de segmentos, entre outros dados. Porém, antes de computar estes dados, foi feita uma análise da literatura de métodos de traçado de biofilamentos. A análise foi realizada para facilitar a compreensão do vasto número de métodos disponíveis, desde componentes individuais (e.g. técnicas de realçe de filamentos) a workflows completas de traçado de biofilamentos. Também há muitas opções de implementações de software. Na análise, foram incluídas 87 publicações envolvendo workflows de traçado de filamentos ou componentes. Para a análise, criou-se uma classificação (10 classes, que incluem interação com o usuário, abordagem teórica, técnica de imageamento, entre outras classes e 120 sub-classes) para apoiar a análise com o uso de conceitos de teoria de grafos. A metodologia proposta poderá ser utilizada no futuro com ferramentas de semântica web e uma base de dados e permitirá analisar um número maior de dados. Desta análise, identificaram-se os métodos mais comuns de melhoramento de imagem (Realçe de filamentos, 44.9%, suavização, 16.3% e Subtração de background 14.3%) e as tendências em abordagens teóricas (e.g. abordagens baseadas em grafos juntas à algoritmos de aprendizado de máquina, realçe de filamentos como o gradient vector flow seguidos de abordagem Levei-set fast-marching). Após a análise da literatura, foram selecionados os métodos de melhoramento mais comuns e avaliados segundo seu impacto na qualidade da imagem. Os testes foram realizados em duas amostras de imagem (experimentos do crescimento de Aspergillus niger de microscopia confocal de varredura a laser) através de um planejamento fatorial completo e análise do índice de similaridade estrutural, SSIM, e razão sinal-ruído, SNR. Resultados mostraram que o algoritmo rolling bali de subtração de background com raio 20 pixels teve o maior efeito positivo em SSIM e SNR no geral. Então, ao utilizar as imagens melhoradas como entrada, foram testados 5 métodos de traçado de filamentos (APP, APP2, NeuTube, NeuronStudio e NeuroGPS-Tree). Os resultados do traçado foram avaliados qualitativamente: O método NeuTube mostrou os resultados visualmente mais acurados. Definiu-se então o método e foram traçadas as imagens completas 3D e no tempo e obtivemos parâmetros morfométricos e da dinâmica do crescimento do fungo (perfis de biomassa e comprimentos totais, por exemplo). Embora se observe que o uso de traçado de filamentos é promisor para obter mais dados do crescimento de fungos filamentosos, discutiu-se a necessidade de aprimorar as técnicas de preparo de amostra e das configurações na aquisição das imagens, de maneira a aumentar a qualidade final das imagens e fornecer resultados mais confiáveis e concretos após o traçado para então tirar conclusões dos dados. Palavras-chave: fungos filamentosos, filamentos biológicos, análise de imagem, traçado de filamentos, melhoramento de imagem.Abstract: Image analysis of biofilaments is becoming an important part of research on biology and biotechnology because it does not only elucidates the morphology of such structures but also gives insights into their development. Additionally, the morphology can be correlated with other variables. For example, image analysis of filamentous fungi allows the correlation of enzyme productivity with different morphologies. We are interested in understanding how the mycelium of a filamentous fungus develops during growth on solid substrates. In order to study that, time-lapsed 3D images of the fungus during growth were obtained, with the intention of computing growth dynamics and morphometric data: colony and tip extension rates, number and positions of branches and tips, segment lengths, among others. However, prior to computing this data, we analysed the literature of biofilament tracing methods. The analysis was done to facilitate the understanding of the vast number of methods available, from single components (e.g. filament enhancement techniques, and specialized model-based approaches) to complete biofilament tracing workflows. There were also many software implementations options. The analysis comprised 87 publications proposing complete biofilament tracing workflows or workflow components. For the analysis, we created a classification methodology (10 main classes, including user interaction, theoretical approach, imaging technique, among other classes and 120 sub-classes) and analysed the publications using graph theory concepts. The proposed methodology could be used in the future with semantic web tools and crowd-sourced web-based databases, allowing the analysis of greater number of data. Out of this analysis, we identified the most common image enhancement methods (Filament enhancement 44.9%, smoothing 16.3%, background subtraction 14.3%) and the theoretical approach trends for biofilament tracing (e.g. graph-based approaches coupled with machine learning algorithms, image enhancement such as gradient vector flows followed by model-based fast marching approach). Following the literature analysis, we selected the most common image enhancement methods to be used prior to biofilament tracing and evaluated their impact on image quality. The tests were done on two sample images (experiments of the growth of Aspergillus niger on two different carbon sources obtained by confocal laser scanning microscopy) through a full factorial design of experiments and analysis of the structural similarity index, SSIM and signal-to-noise ratio, SNR. Results show that background subtraction (Rolling-ball algorithm, 20 pixels radius) had the most positive effect on SSIM and SNR. Then, using the enhanced images as input, we tested 5 different biofilament tracing methods (APP1, APP2, NeuTube, NeuronStudio and NeuroGPS-Tree). We evaluated the tracing results visually and qualitatively: NeuTube was the method with the most visually accurate results. After choosing NeuTube as the best method, we applied it to our complete 3D time-lapsed images and computed some growth dynamics and morphmetric parameters (e.g. biomass profiles, segment and total lengths). Although we indicate that biofilament tracing methods are a promising approach to obtain more data on the growth of the filamentous fungi, we discuss the need to improve the sample preparation techniques and image acquisition set-up in order to increase the quality of the images so the tracing results provide more reliable and concrete results to draw conclusions. Keywords: filamentous fungi, biological filaments, image analysis, filament tracing, image enhancement
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