fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy is a research paper published in Bioinformatics (2017). On theSindex it has a DataRank of 2.8. It has been cited 79 times, with 54 citing works in its 1-hop citation network.
MotivationQuantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.g. high cell densities, cell-to-cell variability and low signal-to-noise ratios.ResultsHere, we evaluate accuracy and speed of various state-of-the-art approaches for cell segmentation in light microscopy images using challenging real and synthetic image data. The results vary between datasets and show that the tested tools are either not robust enough or computationally expensive, thus limiting their application to large-scale experiments. We therefore developed fastER, a trainable tool that is orders of magnitude faster while producing state-of-the-art segmentation quality. It supports various cell types and image acquisition modalities, but is easy-to-use even for non-experts: it has no parameters and can be adapted to specific image sets by interactively labelling cells for training. As a proof of concept, we segment and count cells in over 200 000 brightfield images (1388 × 1040 pixels each) from a six day time-lapse microscopy experiment; identification of over 46 000 000 single cells requires only about two and a half hours on a desktop computer.Availability and implementationC ++ code, binaries and data at https://www.bsse.ethz.ch/csd/software/faster.html [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.657
From this paper's citation signal
Citation Network Contribution
2.2
From 44 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 23% comes from its base citations and 77% from the citation network (44 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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