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 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 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 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 Network-enabled efficient image restoration for 3D microscopy of turbid biological specimens

2020 ◽  
Author(s):  
Le Xiao ◽  
Chunyu Fang ◽  
Yarong Wang ◽  
Tingting Yu ◽  
Yuxuan Zhao ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Science Research ◽  
Light Sheet ◽  
Cell Nuclei ◽  
Two Photon ◽  
Photon Excitation ◽  
3D Fluorescence Microscopy

AbstractThough three-dimensional (3D) fluorescence microscopy has been an essential tool for modern life science research, the light scattering by biological specimens fundamentally prevents its more widespread applications in live imaging. We hereby report a deep-learning approach, termed ScatNet, that enables reversion of 3D fluorescence microscopy from high-resolution targets to low-quality, light-scattered measurements, thereby allowing restoration for a single blurred and light-scattered 3D image of deep tissue, with achieving improved resolution and signal-to-noise ratio. Our approach can computationally extend the imaging depth for current 3D fluorescence microscopes, without the addition of complicated optics. Combining ScatNet approach with cutting-edge light-sheet fluorescence microscopy, we demonstrate that the image restoration of cell nuclei in the deep layer of live Drosophila melanogaster embryos at single-cell resolution. Applying our approach to two-photon excitation microscopy, we could improve the signal and resolution of neurons in mouse brain beyond the photon ballistic region.

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