Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning

2018 ◽  
Vol 17 (2) ◽  
pp. 253-269 ◽  
Author(s):  
Gadea Mata ◽  
Miroslav Radojević ◽  
Carlos Fernandez-Lozano ◽  
Ihor Smal ◽  
Niels Werij ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluorescence Microscopy ◽  
Microscopy Images

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.

scholarly journals Multiframe sure-let denoising of timelapse fluorescence microscopy images

2008 ◽  
Author(s):  
Saskia Delpretti ◽  
Florian Luisier ◽  
Sathish Ramani ◽  
Thierry Blu ◽  
Michael Unser
Keyword(s):  
Fluorescence Microscopy ◽  
Microscopy Images

Data Fusion and Smoothing for Probabilistic Tracking of Viral Structures in Fluorescence Microscopy Images

2021 ◽  
pp. 102168
Author(s):  
C. Ritter ◽  
T. Wollmann ◽  
J.-Y. Lee ◽  
A. Imle ◽  
B. Müller ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Data Fusion ◽  
Probabilistic Tracking ◽  
Microscopy Images ◽  
Viral Structures
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