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SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy
We present SBEMimage, an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. SBEMimage is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitori...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079252/ https://www.ncbi.nlm.nih.gov/pubmed/30108489 http://dx.doi.org/10.3389/fncir.2018.00054 |
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author | Titze, Benjamin Genoud, Christel Friedrich, Rainer W. |
author_facet | Titze, Benjamin Genoud, Christel Friedrich, Rainer W. |
author_sort | Titze, Benjamin |
collection | PubMed |
description | We present SBEMimage, an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. SBEMimage is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitoring, process control, and error handling capabilities improve reliability, speed, and quality of acquisitions. Debris detection, autofocus, real-time image inspection, and various other quality control features minimize the risk of data loss during long-term acquisitions. Adaptive tile selection allows for efficient imaging of large tissue volumes of arbitrary shape. The software’s graphical user interface is optimized for remote operation. In its user-friendly viewport, tile grids covering the region of interest to be acquired are overlaid on previously acquired overview images of the sample surface. Images from other sources, e.g., light microscopes, can be imported and superimposed. SBEMimage complements existing DigitalMicrograph (Gatan Microscopy Suite) installations on 3View systems but permits higher acquisition rates by interacting directly with the microscope’s control software. Its modular architecture and the use of Python/PyQt make SBEMimage highly customizable and extensible, which allows for fast prototyping and will permit adaptation to a wide range of SBEM systems and applications. |
format | Online Article Text |
id | pubmed-6079252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60792522018-08-14 SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy Titze, Benjamin Genoud, Christel Friedrich, Rainer W. Front Neural Circuits Neuroscience We present SBEMimage, an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. SBEMimage is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitoring, process control, and error handling capabilities improve reliability, speed, and quality of acquisitions. Debris detection, autofocus, real-time image inspection, and various other quality control features minimize the risk of data loss during long-term acquisitions. Adaptive tile selection allows for efficient imaging of large tissue volumes of arbitrary shape. The software’s graphical user interface is optimized for remote operation. In its user-friendly viewport, tile grids covering the region of interest to be acquired are overlaid on previously acquired overview images of the sample surface. Images from other sources, e.g., light microscopes, can be imported and superimposed. SBEMimage complements existing DigitalMicrograph (Gatan Microscopy Suite) installations on 3View systems but permits higher acquisition rates by interacting directly with the microscope’s control software. Its modular architecture and the use of Python/PyQt make SBEMimage highly customizable and extensible, which allows for fast prototyping and will permit adaptation to a wide range of SBEM systems and applications. Frontiers Media S.A. 2018-07-31 /pmc/articles/PMC6079252/ /pubmed/30108489 http://dx.doi.org/10.3389/fncir.2018.00054 Text en Copyright © 2018 Titze, Genoud and Friedrich. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Titze, Benjamin Genoud, Christel Friedrich, Rainer W. SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy |
title | SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy |
title_full | SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy |
title_fullStr | SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy |
title_full_unstemmed | SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy |
title_short | SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy |
title_sort | sbemimage: versatile acquisition control software for serial block-face electron microscopy |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079252/ https://www.ncbi.nlm.nih.gov/pubmed/30108489 http://dx.doi.org/10.3389/fncir.2018.00054 |
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