Abstract
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.
Highlights
Large-scale automated image acquisition and analysis methods are spreading in both biomedical and environmental research
To demonstrate the usability of our methods we provide an example application by analysing Fragilariopsis kerguelensis valves originating from a sediment core, which substantially extends the size range reported in the literature
The analysis of large image sets, as they can be produced by slide scanning microscopes, has remained challenging for most diatomists not trained in image analysis
Summary
Large-scale automated image acquisition and analysis methods are spreading in both biomedical and environmental research. Meant to enable highly automated imaging and image analysis workflows, were previously developed for diatom permanent slides during the ADIAC (Automated Diatom Identification And Classification) project [4]. The main reason for this is that both the hard- and software developed in that project are highly customized innovations that were only prototyped, but did not become available to a wider group of users. Development of digitally controlled light microscopes, slide scanning and virtual slide systems, widely available programming libraries for computer vision, machine learning, as well as, more recently, deep convolutional neural networks, are currently making workflows similar to or even beyond those drafted by ADIAC more readily available to a wider user community, including diatomists. The analysis of large image sets, as they can be produced by slide scanning microscopes, has remained challenging for most diatomists not trained in image analysis.
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