Robust open field rodent tracking using a fully convolutional network and a softargmax distance loss
Kopaczka M, Jacob T, Ernst L, Schulz M, Tolba R, Merhof D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer
Pages Range: 242-247
Conference Proceedings Title: Informatik aktuell
Event location: Berlin, DEU
ISBN: 9783658292669
DOI: 10.1007/978-3-658-29267-6_54
Abstract
Analysis of animal locomotion is a commonly used method for analyzing rodent behavior in laboratory animal science. In this context, the open field test is one of the main experiments for assessing treatment effects by analyzing changes in exploratory behavior of laboratory mice and rats. While a number of algorithms for automated analysis of open field experiments has been presented, most of these do not utilize deep learning methods. Therefore, we compare the performance of different deep learning approaches to perform animal localization in open field studies. As our key methodological contribution, we present a novel softargmax-based loss function that can be applied to fully convolutional networks such as the U-Net to allow direct landmark regression from fully convolutional architectures.
Involved external institutions
Germany (DE)How to cite
APA:
Kopaczka, M., Jacob, T., Ernst, L., Schulz, M., Tolba, R., & Merhof, D. (2020). Robust open field rodent tracking using a fully convolutional network and a softargmax distance loss. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 242-247). Berlin, DEU: Springer.
MLA:
Kopaczka, Marcin, et al. "Robust open field rodent tracking using a fully convolutional network and a softargmax distance loss." Proceedings of the International workshop on Algorithmen - Systeme - Anwendungen, 2020, Berlin, DEU Ed. Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm, Springer, 2020. 242-247.
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