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- ItemA guide to super-resolution fluorescence microscopy(New York, NY : Rockefeller Univ. Press, 2010) Schermelleh, L.; Heintzmann, R.; Leonhardt, H.For centuries, cell biology has been based on light microscopy and at the same time been limited by its optical resolution. However, several new technologies have been developed recently that bypass this limit. These new super-resolution technologies are either based on tailored illumination, nonlinear fluorophore responses, or the precise localization of single molecules. Overall, these new approaches have created unprecedented new possibilities to investigate the structure and function of cells. © 2010 Schermelleh et al.
- ItemObject detection networks and augmented reality for cellular detection in fluorescence microscopy(New York, NY : Rockefeller Univ. Press, 2020) Waithe, Dominic; Brown, Jill M.; Reglinski, Katharina; Diez-Sevilla, Isabel; Roberts, David; Eggeling, ChristianObject detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines. © 2020 Waithe et al.