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BeadNet: Deep learning-based bead detection and counting in low-resolution microscopy images

Scherr, Tim; Streule, Karolin; Bartschat, Andreas; Böhland, Moritz; Stegmaier, Johannes; Reischl, Markus; Orian-Rousseau, Véronique; Mikut, Ralf

Abstract:
Motivation
An 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.

Results
In 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.

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Verlagsausgabe §
DOI: 10.5445/IR/1000120804
Veröffentlicht am 03.12.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Biologische und Chemische Systeme (IBCS)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISSN: 1367-4803, 1460-2059
KITopen-ID: 1000120804
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Erschienen in Bioinformatics
Verlag Oxford University Press (OUP)
Band 36
Heft 17
Seiten 4668-4670
Vorab online veröffentlicht am 26.06.2020
Nachgewiesen in Dimensions
Scopus
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