Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification-Reference-Cited by-同舟云学术

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Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Published:2021-03-08 Issue:3 Volume:61 Page:1136-1149
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Yildirim Batuhan¹²³^ORCID,Cole Jacqueline M.¹²³⁴^ORCID

Affiliation:

1. Cavendish Laboratory, Department of Physics, University of Cambridge, J.J. Thomson Avenue, Cambridge CB3 0HE, U.K.

2. ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 OQX, U.K.

3. Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 OQX, U.K.

4. Department of Chemical Engineering and Biotechnology, University of Cambridge, J.J. Thomson Avenue, Cambridge CB3 0AS, U.K.

Funder

Royal Academy of Engineering

BASF

Science and Technology Facilities Council

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c01455

Reference80 articles.

1. ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images

2. ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature

3. Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

4. Automatic segmentation of inorganic nanoparticles in BF TEM micrographs

5. Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform

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