Abstract
We have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based GUI environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near-instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.
Highlights
User-friendly setup and use for simple to complex bespoke microscopes.
Facilitates collaborations between biomedical scientists and microscope technologists.
Touchscreen for near-instantaneous navigation of specimen landscape.
Uses Python-Microscope, for abstracted open source hardware device control.
Well-suited for user training of AI-algorithms for automated microscopy.
Competing Interest Statement
Martin Booth declares a significant interest in Aurox Ltd., whose microscopes were used in this work.
