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Automated phenotype pattern recognition of zebrafish for high-throughput screening

Schutera, M. 1; Dickmeis, T. ORCID iD icon 2; Mione, M. 2; Peravali, R. 2; Marcato, D. 2; Reischl, M. ORCID iD icon 1; Mikut, R. ORCID iD icon 1; Pylatiuk, C. 1
1 Institut für Angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
2 Institut für Toxikologie und Genetik (ITG), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79–99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented.

Verlagsausgabe §
DOI: 10.5445/IR/1000059326
Veröffentlicht am 11.01.2022
DOI: 10.1080/21655979.2016.1197710
Zitationen: 16
Web of Science
Zitationen: 13
Zitationen: 19
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik (IAI)
Institut für Toxikologie und Genetik (ITG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2016
Sprache Englisch
Identifikator ISSN: 2165-5979
KITopen-ID: 1000059326
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Erschienen in Bioengineered
Verlag Taylor & Francis Open Access
Band 7
Heft 4
Seiten 261-265
Vorab online veröffentlicht am 10.06.2016
Schlagwörter feature detection, high-through, put screening, pattern recognition, support vector machine, zebrafish (Danio rerio)
Nachgewiesen in PubMed
Web of Science
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