602 research outputs found

    A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images

    Get PDF
    Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise

    Histopathological image analysis : a review

    Get PDF
    Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

    MitoLoc: A method for the simultaneous quantification of mitochondrial network morphology and membrane potential in single cells.

    Get PDF
    Mitochondria assemble into flexible networks. Here we present a simple method for the simultaneous quantification of mitochondrial membrane potential and network morphology that is based on computational co-localisation analysis of differentially imported fluorescent marker proteins. Established in, but not restricted to, Saccharomyces cerevisiae, MitoLoc reproducibly measures changes in membrane potential induced by the uncoupling agent CCCP, by oxidative stress, in respiratory deficient cells, and in ∆fzo1, ∆ref2, and ∆dnm1 mutants that possess fission and fusion defects. In combination with super-resolution images, MitoLoc uses 3D reconstruction to calculate six geometrical classifiers which differentiate network morphologies in ∆fzo1, ∆ref2, and ∆dnm1 mutants, under oxidative stress and in cells lacking mtDNA, even when the network is fragmented to a similar extent. We find that mitochondrial fission and a decline in membrane potential do regularly, but not necessarily, co-occur. MitoLoc hence simplifies the measurement of mitochondrial membrane potential in parallel to detect morphological changes in mitochondrial networks. Marker plasmid open-source software as well as the mathematical procedures are made openly available.This work was supported by funding from the Wellcome Trust (RG 093735/Z/10/Z) and the ERC (Starting grant 260809). M.R. is a Wellcome Trust Research Career Development and Wellcome-Beit prize fellow.This is the final version. It was first published by Elsevier at http://www.sciencedirect.com/science/article/pii/S1567724915300088

    Quantitative single-molecule mapping of neuronal proteins at the nanoscale

    Get PDF
    The advent of super-resolution microscopy, also called nanoscopy, allowed a substantial improvement of spatial resolution, opening the door for the observation of biological structures beyond the diffraction limit impossible with conventional light microscopy. Among the super-resolution techniques, single-molecule localization microscopies have proven to be a powerful tool to address many biological issues, since they provide an imaging resolution of the order of tens of nanometers and the possibility to perform quantitative measurements. Neuroscience has been one of the fields in biology to benefit most from super-resolution microscopy. During the last years, single-molecule localization microscopies have been widely exploited to study diffraction-limited subcellular structures in neurons, allowing a deeper understanding of molecular mechanisms underlying neural network functioning and its impairments in pathologies. In this thesis, we developed a tool to investigate the distribution, spatial organization, clustering, and density of neural proteins at the nanoscale. In particular, we focused on the quantitative study of synaptic neurotransmitter receptors and focal adhesions. The knowledge of the distribution and stoichiometry of synaptic proteins is fundamental to understand the regulation of the synaptic transmission in neurons. However, a detailed characterization of the protein architecture within synapses can be achieved only by visualizing them at a nanometric level. Here we propose a quantitative approach based on stochastic optical reconstruction microscopy combined with cluster analysis to investigate the molecular rearrangement of GABAA receptors into subsynaptic domains during synaptic plasticity of the inhibitory neurotransmission. This approach was also applied to the study of the adhesion machinery of mammalian cells and neurons at the interface with single-layer graphene to investigate the interaction between cells and nanostructured materials. Due to their excellent properties and biocompatibility, graphene and its derivatives are the ideal candidates for many biomedical applications, such as neural tissue engineering. However, the adhesion processes at the graphene/neuron interface are still not clear nowadays. Our method offers an easy way to study how graphene substrates can affect adhesion and migration of different types of cells

    Exploring variability in medical imaging

    Get PDF
    Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

    Differential Models, Numerical Simulations and Applications

    Get PDF
    This Special Issue includes 12 high-quality articles containing original research findings in the fields of differential and integro-differential models, numerical methods and efficient algorithms for parameter estimation in inverse problems, with applications to biology, biomedicine, land degradation, traffic flows problems, and manufacturing systems

    Meeting at the Membrane – Confined Water at Cationic Lipids & Neuronal Growth on Fluid Lipid Bilayers: Meeting at the Membrane – Confined Water at Cationic Lipids &Neuronal Growth on Fluid Lipid Bilayers

    Get PDF
    Die Zellmembran dient der Zelle nicht nur als Ă€ußere HĂŒlle, sondern ist auch an einer Vielzahl von lebenswichtigen Prozessen wie Signaltransduktion oder ZelladhĂ€sion beteiligt. Wasser als integraler Bestandteil von Zellen und der extrazellulĂ€ren Matrix hat sowohl einen großen Einfluss auf die Struktur von BiomolekĂŒlen, als auch selbst besondere Merkmale in eingschrĂ€nkter Geometrie. Im Rahmen dieser Arbeit wurden zwei Effekte an Modellmembranen untersucht: Erstens der Einfluss des Gegenions an kationischen Lipiden (DODAX, X = F, Cl, Br, I) auf die Eigenschaften des GrenzflĂ€chenwassers und zweitens das Vermögen durch ViskositĂ€tsĂ€nderungen das Wachstum von Nervenzellen anzuregen sowie die einzelnen Stadien der Bildung von neuronalen Netzwerken und deren Optimierung zu charakterisieren. Lipidmultischichten und darin adsorbiertes GrenzflĂ€chenwasser wurden mittels Infrarotspektroskopie mit abgeschwĂ€chter Totalreflexion untersucht. Nach Charakterisierung von Phasenverhalten und WasserkapazitĂ€t der Lipide wurden die Eigenschaften des Wassers durch kontrollierte Hydratisierung bei einem Wassergehalt von einem WassermolekĂŒl pro Lipid verglichen. Durch die geringe WasserkapazitĂ€t können in diesem besonderen System direkte Wechselwirkungen zwischen Lipiden und Wasser aus der ersten Hydratationsschale beobachtet werden. Bemerkenswert strukturierte OH-Streckschwingungsbanden in AbhĂ€ngigkeit des Anions und niedrige IR-Ordnungsparameter zeigen, dass stark geordnete, in ihrer MobilitĂ€t eingeschrĂ€nkte WassermolekĂŒle an DODAX in verschiedenen Populationen mit unterschiedlich starken WasserstoffbrĂŒckenbindungen existieren und sich vermutlich in kleinen Clustern anordnen. Die zweite Fragestellung hatte zum Ziel, das Wachstum von Nervenzellen auf Membranen zu beleuchten. Auf der Ebene einzelner Zellen wurde untersucht, ob sich in Analogie zu den bisher verwendeten elastischen Substraten, die ViskositĂ€t von Membranen als neuartiger physikalischer Stimulus dafĂŒr eignet, das mechanosensitive Verhalten von Neuronen zu modulieren. Das Wachstum der Neuronen wurde auf substrat- und polymergestĂŒtzten Lipiddoppelschichten mittels Phasenkontrastmikroskopie beobachtet. Die Quantifizierung der NeuritenlĂ€ngen, -auswuchsgeschwindigkeiten und -verzweigungen zeigten kaum signifikante Unterschiede. Diffusionsmessungen (FRAP) ergaben, dass entgegen der Erwartungen, die Substrate sehr Ă€hnliche FluiditĂ€ten aufweisen. Die Betrachtung der zeitlichen Entwicklung des kollektiven Neuronenwachstums, also der Bildung von komplexen Netzwerken, offenbarte robuste „Kleine-Welt“-Eigenschaften und darĂŒber hinaus unterschiedliche Stadien. Diese wurden durch graphentheoretische Analyse beschrieben, um anhand typischer GrĂ¶ĂŸen wie dem Clusterkoeffizienten und der kĂŒrzesten PfadlĂ€nge zu zeigen, wie sich die Neuronen in einem frĂŒhen Stadium vernetzen, im Verlauf eine maximale KomplexitĂ€t erreichen und letztlich das Netzwerk durch effiziente Umstrukturierung hinsichtlich kurzer PfadlĂ€ngen optimiert wird

    An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

    Get PDF
    Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

    Histopathological image analysis: a review,”

    Get PDF
    Abstract-Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

    Pattern Recognition

    Get PDF
    A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
    • 

    corecore