SimpliPyTEM: An open-source Python library and app to simplify transmission electron microscopy and in situ-TEM image analysis

Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and larg...

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Vydáno v:PloS one Ročník 18; číslo 10; s. e0285691
Hlavní autoři: Ing, Gabriel, Stewart, Andrew, Battaglia, Guiseppe, Ruiz-Perez, Lorena
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 05.10.2023
Public Library of Science (PLoS)
Témata:
Automation
Computer and Information Sciences
Data processing
Deep learning
Diagnostic imaging
Electron microscopy
Electrons
Engineering and Technology
Evaluation
Image acquisition
Image analysis
Image contrast
Image processing
Image Processing, Computer-Assisted - methods
Image quality
Libraries
Metadata
Microscopy
Microscopy, Electron, Transmission
Mobile Applications
Portable computers
Python
Research and Analysis Methods
Software
Transmission electron microscopy
Workflow
United Kingdom
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0285691