scholarly journals Time-Dependent Image Restoration of Low-SNR Live Cell Ca2+ Fluorescence Microscopy Data

2021 ◽  
Author(s):  
Lena-Marie Woelk ◽  
Sukanya A. Kannabiran ◽  
Valerie Brock ◽  
Christine E. Gee ◽  
Christian Lohr ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Restoration ◽  
Synthetic Data ◽  
Image Data ◽  
Live Cell ◽  
Time Dependent ◽  
Data Sets ◽  
Temporal Consistency ◽  
Spatio Temporal ◽  
Microscopy Data

Live cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems, but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four data sets: synthetic data for in-depth evaluation of the algorithm behavior; two data sets acquired for analysis of initial Ca2+ microdomains in T cells; and, to illustrate transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

scholarly journals Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data

2021 ◽  
Vol 22 (21) ◽  
pp. 11792
Author(s):  
Lena-Marie Woelk ◽  
Sukanya A. Kannabiran  ◽  
Valerie J. Brock  ◽  
Christine E. Gee  ◽  
Christian Lohr  ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Image Data ◽  
Live Cell ◽  
Time Dependent ◽  
Temporal Consistency ◽  
Spatio Temporal ◽  
Microscopy Data

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

scholarly journals 3D fluorescence microscopy data synthesis for segmentation and benchmarking

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260509
Author(s):  
Dennis Eschweiler ◽  
Malte Rethwisch ◽  
Mareike Jarchow ◽  
Simon Koppers ◽  
Johannes Stegmaier
Keyword(s):  
Fluorescence Microscopy ◽  
Image Data ◽  
Training Data ◽  
Cellular Structures ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Automated Generation ◽  
3D Fluorescence Microscopy ◽  
Manual Interaction ◽  
Microscopy Data

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.

scholarly journals Live cell imaging of macrophage/bacterium interaction demonstrates cell lysis induced by Corynebacterium diphtheriae and Corynebacterium ulcerans

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Dulanthi Weerasekera ◽  
Jonas Hahn ◽  
Martin Herrmann ◽  
Andreas Burkovski
Keyword(s):  
Fluorescence Microscopy ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Time Lapse ◽  
Corynebacterium Diphtheriae ◽  
Live Cell ◽  
Human Macrophage ◽  
Data Description ◽  
Corynebacterium Ulcerans ◽  
Microscopy Data

Abstract Objectives In frame of a study to characterize the interaction of human macrophage-like cells with pathogenic corynebacteria, Corynebacterium diphtheriae and Corynebacterium ulcerans, live cell imaging experiments were carried out and time lapse fluorescence microscopy videos were generated, which are presented here. Data description The time lapse fluorescence microscopy data revealed new insights in the interaction of corynebacteria with human macrophage-like THP-1 cells. In contrast to uninfected cells and infections with non-pathogenic C. glutamicum used as a control, pathogenic C. diphtheriae and C. ulcerans showed highly detrimental effects towards human cells and induction of cell death of macrophages.

Non-rigid multi-frame registration of cell nuclei in live cell fluorescence microscopy image data

2015 ◽  
Vol 19 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Marco Tektonidis ◽  
Il-Han Kim ◽  
Yi-Chun M. Chen ◽  
Roland Eils ◽  
David L. Spector ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Data ◽  
Live Cell ◽  
Cell Nuclei ◽  
Microscopy Image

scholarly journals Shape-aware Stochastic Neighbour Embedding for Robust Data Visualisations

2021 ◽  
Author(s):  
Tobias Wängberg ◽  
Chun-Biu Li ◽  
Joanna Tyrcha
Keyword(s):  
Single Cell ◽  
Cluster Structure ◽  
Synthetic Data ◽  
Image Data ◽  
Superior Performance ◽  
Test Cases ◽  
Data Sets ◽  
Transcriptomics Data ◽  
Quantitative Validation ◽  
Graph Distances

Abstract The t-distributed Stochastic Neighbour Embedding (t-SNE) method has emerged as one of the leading methods for visualising High Dimensional (HD) data in a wide variety of fields, especially for revealing cluster structure in HD single cell transcriptomics data. However, several shortcomings of the algorithm have been identified. Specifically, t-SNE is often unable to correctly represent hierarchical relationships between clusters and spurious patterns may arise in the embedding due to incorrect parameter settings, which could lead to misinterpretations of the data. Here we incorporate t-SNE with shape-aware graph distances, a method termed shape-aware stochastic neighbour embedding (SASNE), to mitigate these limitations of the t-SNE. The merits of the SASNE are first demonstrated using synthetic data sets, where we see a significant improvement in embedding imbalanced and nonlinear clusters, as well as preservation of hierarchical structure, based on quantitative validation in clustering and dimensionality reductions. Moreover, we propose a data-driven parameter setting which we find consistently optimal in all test cases. Lastly, we demonstrate the superior performance of SASNE in embedding the MNIST image data and the single cell transcriptomics gene expression data.

scholarly journals The wave surveyor technique for fast plasma wave detection in multi-spacecraft data

2008 ◽  
Vol 26 (7) ◽  
pp. 1699-1710 ◽  
Author(s):  
J. Vogt ◽  
Y. Narita ◽  
O. D. Constantinescu
Keyword(s):  
Synthetic Data ◽  
Large Data ◽  
Wave Mode ◽  
Data Sets ◽  
Near Earth Space ◽  
Spectral Density Matrix ◽  
Analysis Scheme ◽  
Filtering Technique ◽  
Spatio Temporal ◽  
Spacecraft Data

Abstract. Multi-satellite missions like Cluster allow to study the full spatio-temporal variability of plasma processes in near-Earth space, and both the frequency and the wave vector dependence of dispersion relations can be reconstructed. Existing wave analysis methods include high-resolution beamformers like the wave telescope or k-filtering technique, and the phase differencing approach that combines the correlations measured at pairs of sensors of the spacecraft array. In this paper, we make use of the eigendecomposition of the cross spectral density matrix to construct a direct wave identification method that we choose to call the wave surveyor technique. The analysis scheme extracts only the dominant wave mode but is much faster to apply than existing techniques, hence it is expected to ease survey-type detection of waves in large data sets. The wave surveyor technique is demonstrated by means of synthetic data, and is also applied to Cluster magnetometer measurements.

scholarly journals Sharing and reusing cell image data

2018 ◽  
Vol 29 (11) ◽  
pp. 1274-1280 ◽  
Author(s):  
Assaf Zaritsky
Keyword(s):  
Data Analysis ◽  
Image Data ◽  
Hypothesis Generation ◽  
Data Sets ◽  
Scientific Publications ◽  
Data Standardization ◽  
Cell Image ◽  
Huge Data ◽  
Funding Agencies ◽  
Microscopy Data

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.

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