ArticleBioinformatics (Oxford, England)2020
BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- hiPSC-Derived Schwann Cells Influence Myogenic Differentiation in Neuromuscular Cocultures.Cells · 2021Article
- Quantitative Approaches to Study Retinal Neurogenesis.Biomedicines · 2021Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAn automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution images. In addition, expert knowledge is needed to adjust parameters.
resultsIn combination with our image labeling tool, BeadNet enables biologists to easily annotate and process their data reducing the expertise required in many existing image analysis pipelines. BeadNet outperforms state-of-the-art-algorithms in terms of missing, added and total amount of beads. AVAILABILITY AND IMPLEMENTATION: BeadNet (software, code and dataset) is available at https://bitbucket.org/t_scherr/beadnet. The image labeling tool is available at https://bitbucket.org/abartschat/imagelabelingtool. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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