3,774 research outputs found
Stochastic Geometry for Multiple Object Tracking in Fluorescence Microscopy
International audienceThis paper proposes a framework for tracking multiple fluorescent objects in 2D + time video-microscopy. We present a novel batch-processing track-before-detect multiple object tracking approach based on a spatio-temporal marked point process model of ellipses. Our approach takes into account events such as births, deaths, splits and merges of objects which are motivated by the biological and physical considerations. We show the performance of the proposed model on synthetic biological data and a real total internal reflection fluorescence microscopy (TIRF) image sequence
Aberration-free calibration for 3D single molecule localization microscopy
We propose a straightforward sample-based technique to calibrate the axial
detection in 3D single molecule localization microscopy (SMLM). Using
microspheres coated with fluorescent molecules, the calibration curves of
PSF-shaping- or intensity-based measurements can be obtained for any required
depth range from a few hundreds of nanometers to several tens of microns. This
experimental method takes into account the effect of the spherical aberration
without requiring computational correction.Comment: 8 pages, 2 figures. Submitted to Optics Letters on October 12th, 201
Globally Optimal Cell Tracking using Integer Programming
We propose a novel approach to automatically tracking cell populations in
time-lapse images. To account for cell occlusions and overlaps, we introduce a
robust method that generates an over-complete set of competing detection
hypotheses. We then perform detection and tracking simultaneously on these
hypotheses by solving to optimality an integer program with only one type of
flow variables. This eliminates the need for heuristics to handle missed
detections due to occlusions and complex morphology. We demonstrate the
effectiveness of our approach on a range of challenging sequences consisting of
clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor
Image informatics strategies for deciphering neuronal network connectivity
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies
Fast fluorescence microscopy for imaging the dynamics of embryonic development
Live imaging has gained a pivotal role in developmental biology since it increasingly allows real-time observation of cell behavior in intact organisms. Microscopes that can capture the dynamics of ever-faster biological events, fluorescent markers optimal for in vivo imaging, and, finally, adapted reconstruction and analysis programs to complete data flow all contribute to this success. Focusing on temporal resolution, we discuss how fast imaging can be achieved with minimal prejudice to spatial resolution, photon count, or to reliably and automatically analyze images. In particular, we show how integrated approaches to imaging that combine bright fluorescent probes, fast microscopes, and custom post-processing techniques can address the kinetics of biological systems at multiple scales. Finally, we discuss remaining challenges and opportunities for further advances in this field
Machine learning -- based diffractive imaging with subwavelength resolution
Far-field characterization of small objects is severely constrained by the
diffraction limit. Existing tools achieving sub-diffraction resolution often
utilize point-by-point image reconstruction via scanning or labelling. Here, we
present a new imaging technique capable of fast and accurate characterization
of two-dimensional structures with at least wavelength/25 resolution, based on
a single far-field intensity measurement. Experimentally, we realized this
technique resolving the smallest-available to us 180-nm-scale features with
532-nm laser light. A comprehensive analysis of machine learning algorithms was
performed to gain insight into the learning process and to understand the flow
of subwavelength information through the system. Image parameterization,
suitable for diffractive configurations and highly tolerant to random noise was
developed. The proposed technique can be applied to new characterization tools
with high spatial resolution, fast data acquisition, and artificial
intelligence, such as high-speed nanoscale metrology and quality control, and
can be further developed to high-resolution spectroscop
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Challenges of analysing stochastic gene expression in bacteria using single-cell time-lapse experiments.
Stochastic gene expression causes phenotypic heterogeneity in a population of genetically identical bacterial cells. Such non-genetic heterogeneity can have important consequences for the population fitness, and therefore cells implement regulation strategies to either suppress or exploit such heterogeneity to adapt to their circumstances. By employing time-lapse microscopy of single cells, the fluctuation dynamics of gene expression may be analysed, and their regulatory mechanisms thus deciphered. However, a careful consideration of the experimental design and data-analysis is needed to produce useful data for deriving meaningful insights from them. In the present paper, the individual steps and challenges involved in a time-lapse experiment are discussed, and a rigorous framework for designing, performing, and extracting single-cell gene expression dynamics data from such experiments is outlined
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