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Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science.

Mick A Phillips1,2,3, David Miguel Susano Pinto1, Nicholas Hall1

  • 1Micron Advanced Bioimaging Unit, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.

Wellcome Open Research
|June 7, 2023
PubMed
Summary
This summary is machine-generated.

We developed Microscope-Cockpit (Cockpit), a user-friendly Python GUI for controlling microscopes. This open-source software enables rapid slide navigation and automated imaging, enhancing microscopy workflows.

Keywords:
Adaptive optics (AO)Artificial Intelligence (AI)Bespoke microscopeFree and open source softwareImagingMachine Learning (ML)Microscope hardware device controlMicroscope-PythonSuper resolution microscopy

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Area of Science:

  • Microscopy
  • Optical Engineering
  • Software Development

Background:

  • Traditional microscopy control systems can be complex and proprietary.
  • There is a need for adaptable, open-source software for advanced microscopy.
  • Bespoke microscope systems require flexible and user-friendly control environments.

Purpose of the Study:

  • To develop an open-source, Python-based Graphical User Interface (GUI) for microscope control.
  • To enable efficient, eyepiece-less navigation and automated image acquisition.
  • To provide a platform for controlling both simple and complex bespoke microscope systems.

Main Methods:

  • Developed "Microscope-Cockpit" (Cockpit), a Python GUI leveraging "Python-Microscope" (Microscope) for hardware control.
  • Implemented abstracted device models for complex components like deformable mirrors and spatial light modulators.
  • Demonstrated the platform on widefield, adaptive optics, and structured illumination microscopy systems.

Main Results:

  • Cockpit provides near-instantaneous slide navigation and automated acquisition capabilities.
  • The software successfully controls diverse hardware, including adaptive optics and structured illumination.
  • The open-source Python base allows for easy customization and integration of future functionalities, such as machine learning.

Conclusions:

  • Microscope-Cockpit offers a versatile and user-friendly solution for controlling bespoke microscopy systems.
  • The platform streamlines advanced imaging techniques and facilitates future innovation in automated microscopy.
  • Its open-source nature promotes accessibility and adaptability in scientific imaging research.