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dc.contributor.authoral Outa, Amani
dc.contributor.authorHicks, Steven
dc.contributor.authorThambawita, Vajira L B
dc.contributor.authorAndresen, Siri
dc.contributor.authorEnserink, Jorrit Martijn
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorKnævelsrud, Helene
dc.description.abstractCells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target. Image analysis to follow autophagy in cells, especially on high-content screens, has proven to be a bottleneck. Machine learning (ML) algorithms have recently emerged as crucial in analyzing images to efficiently extract information, thus contributing to a better understanding of the questions at hand. this paper presents CELLULaR, an open dataset consisting of images of cells expressing the autophagy reporter mRFP-EGFP-Atg8a with cell-specific segmentation masks. Each cell is annotated into either basal autophagy, activated autophagy, or unknown. Furthermore, we introduce some preliminary experiments using the dataset that can be used as a baseline for future research.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleCELLULAR, A Cell Autophagy Imaging Dataseten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalScientific Dataen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal