Robust Open Field Rodent Tracking Using a Fully Convolutional Network and a Softargmax Distance Loss

Marcin Kopaczka, Tobias Jacob, Lisa Ernst, Mareike Schulz, René Tolba, Dorit Merhof

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.