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dc.contributor.authorKohtala, Sampsa
dc.contributor.authorNedal, Tonje Marie Vikene
dc.contributor.authorCarlo, Kriesi
dc.contributor.authorMoen, Siv Helen
dc.contributor.authorMa, Qianli
dc.contributor.authorØdegaard, Kristin Sirnes
dc.contributor.authorStandal, Therese
dc.contributor.authorSteinert, Martin
dc.description.abstractA balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.en_US
dc.description.abstractAutomated Quantification of Human Osteoclasts Using Object Detectionen_US
dc.publisherFrontiers Mediaen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAutomated Quantification of Human Osteoclasts Using Object Detectionen_US
dc.title.alternativeAutomated Quantification of Human Osteoclasts Using Object Detectionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalFrontiers in Cell and Developmental Biologyen_US

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