dc.date.accessioned | 2024-03-25T17:47:52Z | |
dc.date.available | 2024-03-25T17:47:52Z | |
dc.date.created | 2023-12-12T10:42:01Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | al Outa, Amani Hicks, Steven Thambawita, Vajira L B Andresen, Siri Enserink, Jorrit Martijn Halvorsen, Pål Riegler, Michael Alexander Knævelsrud, Helene . CELLULAR, A Cell Autophagy Imaging Dataset. Scientific Data. 2023 | |
dc.identifier.uri | http://hdl.handle.net/10852/110129 | |
dc.description.abstract | Abstract Cells 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. | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | CELLULAR, A Cell Autophagy Imaging Dataset | |
dc.title.alternative | ENEngelskEnglishCELLULAR, A Cell Autophagy Imaging Dataset | |
dc.type | Journal article | |
dc.creator.author | al Outa, Amani | |
dc.creator.author | Hicks, Steven | |
dc.creator.author | Thambawita, Vajira L B | |
dc.creator.author | Andresen, Siri | |
dc.creator.author | Enserink, Jorrit Martijn | |
dc.creator.author | Halvorsen, Pål | |
dc.creator.author | Riegler, Michael Alexander | |
dc.creator.author | Knævelsrud, Helene | |
cristin.unitcode | 185,53,2,15 | |
cristin.unitname | Senter for kreftcelle-reprogrammering | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 2212208 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific Data&rft.volume=&rft.spage=&rft.date=2023 | |
dc.identifier.jtitle | Scientific Data | |
dc.identifier.volume | 10 | |
dc.identifier.issue | 1 | |
dc.identifier.doi | https://doi.org/10.1038/s41597-023-02687-x | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 2052-4463 | |
dc.type.version | PublishedVersion | |
cristin.articleid | 806 | |
dc.relation.project | NFR/249018 | |
dc.relation.project | NFR/262652 | |
dc.relation.project | NFR/261936 | |
dc.relation.project | EC/H2020/847912 | |
dc.relation.project | NFR/320911 | |
dc.relation.project | EC/H2020/101039174 | |
dc.relation.project | NFR/30078 | |
dc.relation.project | NFR/294916 | |
dc.relation.project | EC/H2020/801133 | |
dc.relation.project | NFR/314811 | |