Bioimage informatics
Franc¸ois Laurent †, Alexandre Blanc †, Lilly May, Lautaro Gandara, Benjamin T. Cocanougher, Benjamin M. W. Jones, Peter Hague, Chloe Barre, Christian L. Vestergaard, Justin Crocker, Marta Zlatic, Tihana Jovanic, Jean-Baptiste Masson
Abstract
Motivation: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical
user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.
Results: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates
the training of a new tagger, achieving similar prediction accuracy using less data.
Availability and implementation: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.
More information at DOI: https://doi.org/10.1093/bioinformatics/btae441
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