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Journal article

SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy.

  • Titze B Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
  • Genoud C Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
  • Friedrich RW Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
  • 2018-08-16
Published in:
  • Frontiers in neural circuits. - 2018
3View
SBEM
SEM
connectomics
imaging software
microtome
serial block-face
volume EM
Animals
Image Processing, Computer-Assisted
Microscopy, Electron, Scanning
Neurosciences
Software
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://susi.usi.ch/global/documents/202453
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