Multi-spot live-image autofocusing for high-throughput microscopy of fluorescently stained bacteria

Screening by automated high-throughput microscopy has become a valuable research tool. An essential component of such systems is the autonomous acquisition of focused images. Here we describe the implementation of a high-precision autofocus routine for imaging of fluorescently stained bacteria on a commercially available microscope. We integrate various concepts and strategies that together substantially enhance the performance of autonomous image acquisition. These are (i) nested focusing in brightfield and fluorescence illumination, (ii) autofocusing by continuous life-image acquisition during movement in z-direction rather than at distinct z-positions, (iii) assessment of the quality and topology of a field of view (FOV) by multi-spot focus measurements and (iv) acquisition of z-stacks and application of an extended depth of field algorithm to compensate for FOV unevenness. The freely provided program and documented source code allow ready adaptation of the here presented approach to various platforms and scientific questions

Visual Servoing-Based approach for efficient autofocusing in Scanning Electron Microscope.

International audienceFast and reliable autofocusing methods are essential for performing automatic nano-objects positioning tasks using a scanning electron microscope (SEM). So far in the literature, various autofocusing algorithms have been proposed utilizing a sharpness measure to compute the best focus. Most of them are based on iterative search approaches; applying the sharpness function over the total range of focus to find an image in-focus. In this paper, a new, fast and direct method of autofocusing has been presented based on the idea of traditional visual servoing to control the focus step using an adaptive gain. The visual control law is validated using a normalized variance sharpness function. The obtained experimental results demonstrate the performance of the proposed autofocusing method in terms of accuracy, speed and robustness

High resolution autofocus for spatial temporal biomedical research

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Large-Scale Permanent Slide Imaging and Image Analysis for Diatom Morphometrics

Light microscopy analysis of diatom frustules is widely used in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. Although there is a need for automation in these applications, various developments in image processing and analysis methodology supporting these tasks have not become widespread in diatom-based analyses. We have addressed this issue by combining our automated diatom image analysis software SHERPA with a commercial slide-scanning microscope. The resulting workflow enables mass-analyses of a broad range of morphometric features from individual frustules mounted on permanent slides. Extensive automation and internal quality control of the results helps to minimize user intervention, but care was taken to allow the user to stay in control of the most critical steps (exact segmentation of valve outlines and selection of objects of interest) using interactive functions for reviewing and revising results. In this contribution, we describe our workflow and give an overview of factors critical for success, ranging from preparation and mounting through slide scanning and autofocus finding to final morphometric data extraction. To demonstrate the usability of our methods we finally provide an example application by analysing Fragilariopsis kerguelensis valves originating from a sediment core, which substantially extends the size range reported in the literature