Microscope Upcycling: Transforming legacy microscopes into automated cloud-integrated imaging systems.
Abstract
Computerized microscopes improve repeatability, throughput, antisepsis, data analysis and data sharing in the biological laboratory, but these machines are cost-prohibitive in most academic environments. This is a barrier into collecting the large and consistent datasets required for machine learning analyses of microscopy data. We demonstrate hardware modifications and software to bring the features of modern computerized microscopes to decades-old legacy laboratory inverted microscopes. We demonstrate automation of X-Y positioning, focus stacking, image acquisition and image storage.
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