Pushing the limits of fluorescence microscopy with adaptive imaging and machine learning (Conference Presentation)

2020 ◽  
Author(s):  
Loic Royer
Keyword(s):  
Machine Learning ◽  
Fluorescence Microscopy ◽  
Adaptive Imaging

scholarly journals Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Daniela M. Borgmann ◽  
Sandra Mayr ◽  
Helene Polin ◽  
Susanne Schaller ◽  
Viktoria Dorfer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluorescence Microscopy ◽  
Single Molecule ◽  
Single Molecule Fluorescence ◽  
D Antigen ◽  
Single Molecule Fluorescence Microscopy

scholarly journals Fluorescence Microscopy Datasets for Training Deep Neural Networks

2020 ◽  
Author(s):  
Guy M. Hagen ◽  
Justin Bendesky ◽  
Rosa Machado ◽  
Tram-Anh Nguyen ◽  
Tanmay Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Quality Data ◽  
Short Exposure ◽  
Biological Research ◽  
High Quality ◽  
Learning Methods ◽  
High Quality Data

AbstractBackgroundFluorescence microscopy is an important technique in many areas of biological research. Two factors which limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging, and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal to noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample.FindingsTo employ deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high quality data sets which can be used to train and evaluate deep learning methods under development.ConclusionThe availability of high quality data is vital for training convolutional neural networks which are used in current machine learning approaches.

scholarly journals TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning

Patterns ◽  
2021 ◽  
pp. 100367
Author(s):  
Parker Edwards ◽  
Kristen Skruber ◽  
Nikola Milićević ◽  
James B. Heidings ◽  
Tracy-Ann Read ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Analysis ◽  
Fluorescence Microscopy ◽  
Microscopy Images

Myelin detection in fluorescence microscopy images using machine learning

2020 ◽  
Vol 346 ◽  
pp. 108946 ◽  
Author(s):  
Sibel Çimen Yetiş ◽  
Abdulkerim Çapar ◽  
Dursun A. Ekinci ◽  
Umut E. Ayten ◽  
Bilal E. Kerman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluorescence Microscopy ◽  
Microscopy Images

scholarly journals Bringing TrackMate in the era of machine-learning and deep-learning.

2021 ◽  
Author(s):  
Dmitry Ershov ◽  
Minh-Son Phan ◽  
Joanna W. Pylvänäinen ◽  
Stéphane U Rigaud ◽  
Laure Le Blanc ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Learning Algorithms ◽  
Improve Performance ◽  
Automated Tracking ◽  
Wide Range ◽  
Bio Imaging ◽  
Microscopy Images

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

scholarly journals Protocol for applying Machine Learning models for the transformation of conventional fluorescence images to super-resolution

2021 ◽  
Author(s):  
Vadim Zinchuk ◽  
Olga Grossenbacher-Zinchuk
Keyword(s):  
Machine Learning ◽  
Fluorescence Microscopy ◽  
Super Resolution ◽  
Learning Models ◽  
Conventional Fluorescence Microscopy ◽  
Mobile Computers ◽  
Analytical Tools ◽  
Image Details ◽  
Microscopy Images ◽  
Machine Learning Models

Abstract Machine Learning offers the opportunity to visualize the invisible in conventional fluorescence microscopy images by improving their resolution while preserving and enhancing image details. This protocol describes the application of GAN-based Machine Learning models to transform the resolution of conventional fluorescence microscopy images to a resolution comparable with super-resolution. It provides a flexible environment using a modern app functioning on both desktop and mobile computers. This approach can be extended for use on other types of microscopy images empowering life science researchers with modern analytical tools.

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