371 research outputs found

    Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system

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    <p>Abstract</p> <p>Background</p> <p>Cell motility is a critical parameter in many physiological as well as pathophysiological processes. In time-lapse video microscopy, manual cell tracking remains the most common method of analyzing migratory behavior of cell populations. In addition to being labor-intensive, this method is susceptible to user-dependent errors regarding the selection of "representative" subsets of cells and manual determination of precise cell positions.</p> <p>Results</p> <p>We have quantitatively analyzed these error sources, demonstrating that manual cell tracking of pancreatic cancer cells lead to mis-calculation of migration rates of up to 410%. In order to provide for objective measurements of cell migration rates, we have employed multi-target tracking technologies commonly used in radar applications to develop fully automated cell identification and tracking system suitable for high throughput screening of video sequences of unstained living cells.</p> <p>Conclusion</p> <p>We demonstrate that our automatic multi target tracking system identifies cell objects, follows individual cells and computes migration rates with high precision, clearly outperforming manual procedures.</p

    A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging

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    Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering. In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract Foreword and Acknowledgements Overview and Contributions Part 1 - Introduction 1 Fluorescence Microscopy 2 Introduction to Visual Processing 3 A Short Introduction to Cross Reality 4 Eye Tracking and Gaze-based Interaction Part 2 - VR and AR for System Biology 5 scenery — VR/AR for Systems Biology 6 Rendering 7 Input Handling and Integration of External Hardware 8 Distributed Rendering 9 Miscellaneous Subsystems 10 Future Development Directions Part III - Case Studies C A S E S T U D I E S 11 Bionic Tracking: Using Eye Tracking for Cell Tracking 12 Towards Interactive Virtual Reality Laser Ablation 13 Rendering the Adaptive Particle Representation 14 sciview — Integrating scenery into ImageJ2 & Fiji Part IV - Conclusion 15 Conclusions and Outlook Backmatter & Appendices A Questionnaire for VR Ablation User Study B Full Correlations in VR Ablation Questionnaire C Questionnaire for Bionic Tracking User Study List of Tables List of Figures Bibliography Selbstständigkeitserklärun

    Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

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    Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods

    Etude expérimentale des dynamiques temporelles du comportement normal et pathologique chez le rat et la souris

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    155 p.Modern neuroscience highlights the need for designing sophisticated behavioral readout of internal cognitive states. From a thorough analysis of classical behavioral test, my results supports the hypothesis that sensory ypersensitivity might be the cause of other behavioural deficits, and confirm the potassium channel BKCa as a potentially relevant molecular target for the development of drug medication against Fragile X Syndrome/Autism Spectrum Disorders. I have also used an innovative device, based on pressure sensors that can non-invasively detect the slightest animal movement with unprecedented sensitivity and time resolution, during spontaneous behaviour. Analysing this signal with sophisticated computational tools, I could demonstrate the outstanding potential of this methodology for behavioural phenotyping in general, and more specifically for the investigation of pain, fear or locomotion in normal mice and models of neurodevelopmental and neurodegenerative disorders

    27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

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    Digital Holography Microscopy at Lab-on-a-Chip scale: novel algorithms and recording strategies

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    Il lavoro presentato è mirato allo sviluppo di nuove tecniche di microscopia olografica digitale (Digital Holography Microscopy, DHM), e di opportuni algoritmi numerici per lo studio di biomateriali in ambiente microfluidico. Nello specifico vengono affrontate due problematiche di imaging particolarmente rilevanti nello studio di sistemi Lab-on-a-Chip (LoC). Dapprima è stato studiato il problema della microscopia quantitativa di oggetti biologici osservati attraverso mezzi complessi, come soluzioni torbide e substrati diffondenti, dove la formazione dell’immagine è ostacolata da processi di scattering. Lo studio condotto è stato mirato all’analisi di processi di diffusione da layer statico e da mezzo liquido di tipo colloidale, in regime quasi-statico e dinamico. Sono stati sviluppati a tale scopo dei metodi di registrazione e nuovi algoritmi di ricostruzione dell’immagine olografica (Multi-Look Digital Holography, MLDH) che consentono di fornire un imaging quantitativo dei campioni in esame. Di particolare interesse è il caso di volumi di liquido costituiti da globuli rossi: nel lavoro presentato viene dimostrata la possibilità di studiare, mediante MLDH, processi di adesione cellulare di materiale biologico situato in presenza di flussi di globuli rossi ad alta concentrazione. La possibilità di visualizzare e analizzare quantitativamente materiale biologico all’interno di un capillare o una vena, compensando l’effetto di diffusione del sangue, potrebbe in futuro consentire di studiare la formazione all’interno del vaso di coaguli e placche di colesterolo, sintomatici dell’insorgere di malattie cardiache. La stessa tecnica è in grado di recuperare l’informazione distorta a causa della presenza all’interno del canale di ostacoli statici o quasi-statici (dovuti alla formazione di bio-film o sospensioni batteriche, o causata da processi di fabbricazione del canale microfluidico), aumentando così notevolmente la varietà dei processi biologici analizzabili su piattaforme LoC. Nel lavoro viene anche dimostrato come la presenza di un mezzo torbido possa essere sfruttata vantaggiosamente al fine di migliorare la qualità dell’immagine in sistemi di imaging basati su luce coerente. Parallelamente è stata messa a punto una tecnica interferometrica che, sfruttando il movimento dei campioni nei canali microfluidici, consente di sostituire un sensore convenzionale 2D con un sensore lineare, più compatto e integrabile a bordo del chip, e capace di fornire prestazioni superiori in termini di velocità di acquisizione. Il lavoro presentato descrive il processo di sintesi di un nuovo tipo di ologramma (Space-Time Digital Hologram, STDH), che consente di ottenere un Field-of-View (FoV) illimitato nella direzione del flusso e, quindi, di superare il trade-off esistente tra fattore di ingrandimento e FoV, comune ad ogni tecnica di microscopia convenzionale. Viene inoltre dimostrato che un STDH mantiene le caratteristiche e i vantaggi di un ologramma digitale standard, quali la focalizzazione numerica flessibile, che permette di analizzare contemporaneamente tutti gli oggetti presenti in un volume di liquido, e la possibilità di estrarre la segnatura di fase degli stessi

    Scalable Inference for Multi-Target Tracking of Proliferating Cells

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    With the continuous advancements in microscopy techniques such as improved image quality, faster acquisition and reduced photo-toxicity, the amount of data recorded in the life sciences is rapidly growing. Clearly, the size of the data renders manual analysis intractable, calling for automated cell tracking methods. Cell tracking – in contrast to other tracking scenarios – exhibits several difficulties: low signal to noise ratio in the images, high cell density and sometimes cell clusters, radical morphology changes, but most importantly cells divide – which is often the focus of the experiment. These peculiarities have been targeted by tracking-byassignment methods that first extract a set of detection hypotheses and then track those over time. Improving the general quality of these cell tracking methods is difficult, because every cell type, surrounding medium, and microscopy setting leads to recordings with specific properties and problems. This unfortunately implies that automated approaches will not become perfect any time soon but manual proof reading by experts will remain necessary for the time being. In this thesis we focus on two different aspects, firstly on scaling previous and developing new solvers to deal with longer videos and more cells, and secondly on developing a specialized pipeline for detecting and tracking tuberculosis bacteria. The most powerful tracking-by-assignment methods are formulated as probabilistic graphical models and solved as integer linear programs. Because those integer linear programs are in general NP-hard, increasing the problem size will lead to an explosion of computational cost. We begin by reformulating one of these models in terms of a constrained network flow, and show that it can be solved more efficiently. Building on the successful application of network flow algorithms in the pedestrian tracking literature, we develop a heuristic to integrate constraints – here for divisions – into such a network flow method. This allows us to obtain high quality approximations to the tracking solution while providing a polynomial runtime guarantee. Our experiments confirm this much better scaling behavior to larger problems. However, this approach is single threaded and does not utilize available resources of multi-core machines yet. To parallelize the tracking problem we present a simple yet effective way of splitting long videos into intervals that can be tracked independently, followed by a sparse global stitching step that resolves disagreements at the cuts. Going one step further, we propose a microservices based software design for ilastik that allows to distribute all required computation for segmentation, object feature extraction, object classification and tracking across the nodes of a cluster or in the cloud. Finally, we discuss the use case of detecting and tracking tuberculosis bacteria in more detail, because no satisfying automated method to this important problem existed before. One peculiarity of these elongated cells is that they build dense clusters in which it is hard to outline individuals. To cope with that we employ a tracking-by-assignment model that allows competing detection hypotheses and selects the best set of detections while considering the temporal context during tracking. To obtain these hypotheses, we develop a novel algorithm that finds diverseM- best solutions of tree-shaped graphical models by dynamic programming. First experiments with the pipeline indicate that it can greatly reduce the required amount of human intervention for analyzing tuberculosis treatment

    Computing with Simulated and Cultured Neuronal Networks

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    Ph.DDOCTOR OF PHILOSOPH
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