Using Machine Learning for Automatic Estimation of M. Smegmatis Cell Count from Fluorescence Microscopy Images

2019 ◽  
pp. 57-68 ◽  
Author(s):  
Daniel Vente ◽  
Ognjen Arandjelović ◽  
Vincent O. Baron ◽  
Evelin Dombay ◽  
Stephen H. Gillespie
Keyword(s):  
Machine Learning ◽  
Fluorescence Microscopy ◽  
Cell Count ◽  
Automatic Estimation ◽  
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.

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

Identifying Top Predictors of Change in Noncalcified Coronary Burden in Psoriasis by Machine Learning Over 1-Year

2021 ◽  
pp. 247553032110007
Author(s):  
Eric Munger ◽  
Amit K. Dey ◽  
Justin Rodante ◽  
Martin P. Playford ◽  
Alexander V. Sorokin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Blood Cell ◽  
Cell Count ◽  
White Blood Cell Count ◽  
White Blood Cell ◽  
Severity Index ◽  
Blood Cell Count ◽  
Psoriasis Area Severity Index ◽  
Severity Index Score

Background: Psoriasis is associated with accelerated non-calcified coronary plaque burden (NCB) by coronary computed tomography angiography (CCTA). Machine learning (ML) algorithms have been shown to effectively identify cardiometabolic variables with NCB in cross-sectional analysis. Objective: To use ML methods to characterize important predictors of change in NCB by CCTA in psoriasis over 1-year of observation. Methods: The analysis included 182 consecutive patients with 80 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative, a prospective, observational cohort study at baseline and 1-year using the random forest regression algorithm. NCB was assessed at baseline and 1-year from CCTA. Results: Using ML, we identified variables of high importance in the context of predicting changes in NCB. For the cohort that worsened NCB (n = 102), top baseline variables were cholesterol (total and HDL), white blood cell count, psoriasis area severity index score, and diastolic blood pressure. Top predictors of 1-year change were change in visceral adiposity, white blood cell count, total cholesterol, c-reactive protein, and absolute lymphocyte count. For the cohort that improved NCB (n = 80), the top baseline variables were HDL cholesterol related including apolipoprotein A1, basophil count, and psoriasis area severity index score, and top predictors of 1-year change were change in apoA, apoB, and systolic blood pressure. Conclusion: ML methods ranked predictors of progression and regression of NCB in psoriasis over 1 year providing strong evidence to focus on treating LDL, blood pressure, and obesity; as well as the importance of controlling cutaneous disease in psoriasis.

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
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