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

scholarly journals Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

2009 ◽  
Vol 96 (8) ◽  
pp. 3379-3389 ◽  
Author(s):  
Richard A. Russell ◽  
Niall M. Adams ◽  
David A. Stephens ◽  
Elizabeth Batty ◽  
Kirsten Jensen ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Fluorescence Microscopy ◽  
Nuclear Architecture ◽  
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.

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