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Deep Fish: Deep Learning-Based Classification of Zebrafish Deformation for High-Throughput Screening
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-5129-530X
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
2017 (English)In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 22, no 1, 102-107 p.Article in journal (Refereed) Published
Abstract [en]

Zebrafish (Danio rerio) is an important vertebrate model organism in biomedical research, especially suitable for morphological screening due to its transparent body during early development. Deep learning has emerged as a dominant paradigm for data analysis and found a number of applications in computer vision and image analysis. Here we demonstrate the potential of a deep learning approach for accurate high-throughput classification of whole-body zebrafish deformations in multifish microwell plates. Deep learning uses the raw image data as an input, without the need of expert knowledge for feature design or optimization of the segmentation parameters. We trained the deep learning classifier on as few as 84 images (before data augmentation) and achieved a classification accuracy of 92.8% on an unseen test data set that is comparable to the previous state of the art (95%) based on user-specified segmentation and deformation metrics. Ablation studies by digitally removing whole fish or parts of the fish from the images revealed that the classifier learned discriminative features from the image foreground, and we observed that the deformations of the head region, rather than the visually apparent bent tail, were more important for good classification performance.

Place, publisher, year, edition, pages
2017. Vol. 22, no 1, 102-107 p.
National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-309535DOI: 10.1177/1087057116667894ISI: 000394206000012PubMedID: 27613194OAI: oai:DiVA.org:uu-309535DiVA: diva2:1051990
Funder
Swedish Research Council, 2012-4968eSSENCE - An eScience Collaboration
Available from: 2016-12-05 Created: 2016-12-05 Last updated: 2017-03-31Bibliographically approved

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Ishaq, OmerSadanandan, Sajith KecherilWählby, Carolina
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