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    Introducing CNN-Based Mouse Grim Scale Analysis for Fully Automated Image-Based Assessment of Distress in Laboratory Mice
    (The Eurographics Association, 2018) Kopaczka, Marcin; Ernst, Lisa; Schock, Justus; Schneuing, Arne; Guth, Alexander; Tolba, Rene; Merhof, Dorit; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    International standards require close monitoring of distress of animals undergoing laboratory experiments in order to minimize the stress level and allow choosing minimally stressful procedures for the experiments. Currently, one of the the best established severity assessment procedures is the mouse grimace scale (MGS), a protocol in which images of the animals are taken and scored by assessing five key visual features that have been shown to be highly correlated with distress and pain. While proven to be highly reliable, MGS assessment is currently a time-consuming task requiring manual video processing for key frame extraction and subsequent expert grading. Additionally, due to the the high per-picture expert time required, MGS scoring is performed on a small number of selected frames from a video. To address these shortcomings, we introduce a method for fully automated real-time MGS scoring of orbital eye tightening, one of the five sub-scores. We define and evaluate the method which is centered around a set of convolutional neural networks (CNNs) and allows live continuous MGS assessment of a mouse in real time. We additionally describe a multithreaded client-server architecture with a graphical user interface that allows convenient use of the developed method for simultaneous real-time MGS scoring of several animals.
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    Annotated Dendrograms for Neurons From the Larval Fruit Fly Brain
    (The Eurographics Association, 2018) Strauch, Martin; Hartenstein, Volker; Andrade, Ingrid V.; Cardona, Albert; Merhof, Dorit; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Recent advances in neuroscience have made it possible to reconstruct all neurons of an entire neural circuit in the larval fruit fly brain from serial electron microscopy image stacks. The reconstructed neurons are morphologically complex 3D graphs whose nodes are annotated with labels representing different types of synapses. Here, we propose a method to draw simplified, yet realistic 2D neuron sketches of insect neurons in order to help biologists formulate hypotheses on neural function at the microcircuit level. The sketches are dendrograms that capture a neuron's branching structure and that preserve branch lengths, providing realistic estimates for distances and signal travel times between synapses. To improve readability of the often densely clustered synapse annotations, synapses are automatically summarized in local clusters of synapses of the same type and arranged to minimize label overlap. We show that two major neuron classes of an olfactory circuit in the larval fruit fly brain can be discriminated visually based on the dendrograms. Unsupervised and supervised data analysis reveals that class discrimination can be performed using morphological features derived from the dendrograms.