Introducing CNN-Based Mouse Grim Scale Analysis for Fully Automated Image-Based Assessment of Distress in Laboratory Mice

Simple item page
dc.contributor.authorKopaczka, Marcinen_US
dc.contributor.authorErnst, Lisaen_US
dc.contributor.authorSchock, Justusen_US
dc.contributor.authorSchneuing, Arneen_US
dc.contributor.authorGuth, Alexanderen_US
dc.contributor.authorTolba, Reneen_US
dc.contributor.authorMerhof, Doriten_US
dc.contributor.editorPuig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pauen_US
dc.date.accessioned2018-09-19T15:19:29Z
dc.date.available2018-09-19T15:19:29Z
dc.date.issued2018
dc.description.abstractInternational 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.en_US
dc.description.sectionheadersHead and Brain
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20181234
dc.identifier.isbn978-3-03868-056-7
dc.identifier.issn2070-5786
dc.identifier.pages101-106
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20181234
dc.identifier.urihttps://doi.org/10.2312/vcbm.20181234
dc.publisherThe Eurographics Associationen_US
dc.titleIntroducing CNN-Based Mouse Grim Scale Analysis for Fully Automated Image-Based Assessment of Distress in Laboratory Miceen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
101-106.pdf
Size:
1.53 MB
Format:
Adobe Portable Document Format
Download
Collections
VCBM 18: Eurographics Workshop on Visual Computing for Biology and Medicine