Mathematical Methods for the Quantification of Actin-Filaments in Microscopic Images

In cell biology confocal laser scanning microscopic images of the actin filament of human osteoblasts are produced to assess the cell development. This thesis aims at an advanced approach for accurate quantitative measurements about the morphology of the bright-ridge set of these microscopic images and thus about the actin filament. Therefore automatic preprocessing, tagging and quantification interplay to approximate the capabilities of the human observer to intuitively recognize the filaments correctly. Numerical experiments with random models confirm the accuracy of this approach

Model-based Curvilinear Network Extraction and Tracking toward Quantitative Analysis of Biopolymer Networks

Curvilinear biopolymer networks pervade living systems. They are routinely imaged by fluorescence microscopy to gain insight into their structural, mechanical, and dynamic properties. Image analysis can facilitate understanding the mechanisms of their formation and their biological functions from a quantitative viewpoint. Due to the variability in network geometry, topology and dynamics as well as often low resolution and low signal-to-noise ratio in images, segmentation and tracking networks from these images is challenging. In this dissertation, we propose a complete framework for extracting the geometry and topology of curvilinear biopolymer networks, and also tracking their dynamics from multi-dimensional images. The proposed multiple Stretching Open Active Contours (SOACs) can identify network centerlines and junctions, and infer plausible network topology. Combined with a $k$-partite matching algorithm, temporal correspondences among all the detected filaments can be established. This work enables statistical analysis of structural parameters of biopolymer networks as well as their dynamics. Quantitative evaluation using simulated and experimental images demonstrate its effectiveness and efficiency. Moreover, a principled method of optimizing key parameters without ground truth is proposed for attaining the best extraction result for any type of images. The proposed methods are implemented into a usable open source software ``SOAX\u27\u27. Besides network extraction and tracking, SOAX provides a user-friendly cross-platform GUI for interactive visualization, manual editing and quantitative analysis. Using SOAX to analyze several types of biopolymer networks demonstrates the potential of the proposed methods to help answer key questions in cell biology and biophysics from a quantitative viewpoint

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

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

The Mycelium as a network

The characteristic growth pattern of fungal mycelia as an interconnected network has a major impact on how cellular events operating on a micron scale affect colony behavior at an ecological scale. Network structure is intimately linked to flows of resources across the network that in turn modify the network architecture itself. This complex interplay shapes the incredibly plastic behavior of fungi and allows them to cope with patchy, ephemeral resources, competition, damage, and predation in a manner completely different from multicellular plants or animals. Here, we try to link network structure with impact on resource movement at different scales of organization to understand the benefits and challenges of organisms that grow as connected networks. This inevitably involves an interdisciplinary approach whereby mathematical modeling helps to provide a bridge between information gleaned by traditional cell and molecular techniques or biophysical approaches at a hyphal level, with observations of colony dynamics and behavior at an ecological level

Development of microfluidic tools to reproduce and characterize the tumor microenvironment

A pesar de que la incidencia del cáncer está en aumento, sobre todo en los países desarrollados, el desarrollo de nuevos fármacos contra esta enfermedad es cada vez menos efectivo. Para revertir esta tendencia, aparece la necesidad de desarrollar mejores herramientas para reproducir y caracterizar el microentorno tumoral. Una de ellas son modelos in vitro más precisos.En este contexto, la microfluídica se presenta como una potente alternativa para el desarrollo de estos nuevos modelos in vitro más precisos, que puedan emplearse para un desarrollo y selección de fármacos más racional y efectivo. No obstante, se trata de un conjunto de técnicas poco extendido en los laboratorios de biología molecular. Así, en la presente tesis se desarrollan dos modelos microfluídicos del microentorno tumoral para tumores sólidos, junto a las herramientas necesarias para su caracterización, todo ello de fácil uso para tratar de generalizar la aplicación de los mismos.En el capítulo 1 se realiza una revisión del estado de la cuestión en lo referente a modelos de cáncer in vitro y su caracterización. En el capítulo 2 se desarrolla un modelo microfluídico de co-cultivo que permite estudiar las interacciones endotelio-tumor, así como la capacidad de penetración y erradicación de células tumorales de nuevos fármacos. En el capítulo 3 se presenta una herramienta para caracterizar los niveles de oxígeno molecular en cualquier punto de un cultivo in vitro 3D. En el capítulo 4 se presenta un modelo de tumor centrado en la generación y caracterización de gradientes biológicos, así como su adaptación a las técnicas tradicionales de biología molecular para el análisis del perfil genético del microentorno tumoral a lo largo del tiempo. Para generar los sistemas microfluídicos descritos anteriormente, se emplearon dispositivos fabricados mediante distintas técnicas y materiales. En los dispositivos se sembraron distintas poblaciones celulares, intentando así reproducir la estructura y organización de los tejidos biológicos. Mediante diferentes técnicas de microscopía (óptica, fluorescencia, confocal, imagen en tiempo real) y sondas fluorescentes se monitorizó la evolución y comportamiento celular. La caracterización del hidrogel sensible al oxígeno se realizó a través de las técnicas ya citadas, así como espectrofotometría, microscopía de fuerza atómica y electrónica de barrido en condiciones ambientales. Finalmente, la extracción de las células de los hidrogeles se realizó por medio de degradaciones enzimáticas, y la cuantificación de la expresión génica mediante extracción de RNA, retrotranscripción y reacción en cadena de la polimerasa cuantitativa.La conclusión general de la tesis, es que la utilización de modelos biomiméticos cambia dramáticamente el resultado de los ensayos realizados in vitro, por lo que su uso es necesario para obtener resultados relevantes y trasladables a la clínica. Asimismo, el desarrollo de sistemas biomiméticos in vitro del microentorno tumoral de uso generalizado es posible mediante el desarrollo de dispositivos de fácil uso, así como del establecimiento de métodos robustos de caracterización de los mismos, tanto in situ como “aguas abajo” del establecimiento de los modelos. Bibliografía: 1. Balkwill FR, Capasso M, Hagemann T (2012) The tumor microenvironment at a glance. J Cell Sci 125: 5591-5596.2. Junttila MR, de Sauvage FJ (2013) Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501: 346-354.3. Scannell JW, Blanckley A, Boldon H, Warrington B (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11: 191-200.4. Adriani G, Pavesi A, Tan AT, Bertoletti A, Thiery JP, et al. (2016) Microfluidic models for adoptive cell-mediated cancer immunotherapies. Drug Discov Today 21: 1472-1478.5. Ayuso JM, Virumbrales-Munoz M, Lacueva A, Lanuza PM, Checa-Chavarria E, et al. (2016) Development and characterization of a microfluidic model of the tumour microenvironment. Sci Rep 6: 36086.6. Ayuso JM, Monge R, Martínez-González A, Virumbrales-Muñoz M, Llamazares GA, et al. (2017) Glioblastoma on a microfluidic chip: Generating pseudopalisades and enhancing aggressiveness through blood vessel obstruction events. Neuro-Oncology: now230.<br /

Biomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based

3D Quantification and Description of the Developing Zebrafish Cranial Vasculature

Background: Zebrafish are an excellent model to study cardiovascular development and disease. Transgenic reporter lines and state-of-the-art microscopy allow 3D visualization of the vasculature in vivo. Previous studies relied on subjective visual interpretation of vascular topology without objective quantification. Thus, there is the need to develop analysis approaches that model and quantify the zebrafish vasculature to understand the effect of development, genetic manipulation or drug treatment. Aim: To establish an image analysis pipeline to extract quantitative 3D parameters describing the shape and topology of the zebrafish vasculature, and examine how these are impacted during development, disease, and by chemicals. Methods: Experiments were performed in zebrafish embryos, conforming with UK Home Office regulations. Image acquisition of transgenic zebrafish was performed using a Z.1 Zeiss light-sheet fluorescence microscope. Pre-processing, enhancement, registration, segmentation, and quantification methods were developed and optimised using open-source software, Fiji (Fiji 1.51p; National Institutes of Health, Bethesda, USA). Results: Motion correction was successfully applied using Scale Invariant Feature Transform (SIFT), and vascular enhancement based on vessel tubularity (Sato filter) exceeded general filter outcomes. Following evaluation and optimisation of a variety of segmentation methods, intensity-based segmentation (Otsu thresholding) was found to deliver the most reliable segmentation, allowing 3D vascular volume measurement. Following successful segmentation of the cerebral vasculature, a workflow to quantify left-right intra-sample symmetry was developed, finding no difference from 2-to-5dpf. Next, the first vascular inter-sample registration using a manual landmark-based approach was developed and it was found that conjugate direction search allowed automatic inter-sample registration. This enabled extraction of age-specific regions of similarity and variability between different individual embryos from 2-to-5dpf. A workflow was developed to quantify vascular network length, branching points, diameter, and complexity, showing reductions in zebrafish without blood flow. Also, I discovered and characterised a previously undescribed endothelial cell membrane behaviour termed kugeln. Conclusion: A workflow that successfully extracts the zebrafish vasculature and enables detailed quantification of a wide variety of vascular parameters was developed