Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks 2024-175-1 https://doi.org/10.48723/srtg-ss33 Swedish National Data Service Svensk nationell datatjänst Landing page Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks 2024-175-1 https://doi.org/10.48723/srtg-ss33 oai:swepub.ki.se:906885 10.1038/s42003-025-07766-w Sharma, Osheen Sharma, Osheen Seashore-Ludlow, Brinton Seashore-Ludlow, Brinton 2021-03420_VR 2021-03420_VR M19-0271 M19-0271 KID, 2020-01096 KID, 2020-01096 Swedish National Data Service Svensk nationell datatjänst Landing page This dataset provides detailed imaging data from various co-culture assays of ovarian cancer and fibroblast cell lines, treated with a wide range of drugs. The structured organization and comprehensive naming conventions allow for easy navigation and analysis of the data. The images are treated with 528 drugs from FIMM Oncology Library to study the drug effect on cancer cell morphology in presence of fibroblasts. The dataset comprises images from 2D coculture high-content screening data in .tiff format. The images were acquired using the Opera Phenix at a 10x magnification. It includes a total of 245,760 raw images (including 4 field of views), each with a resolution of 1080x1080 pixels. For initial analysis, the images were read directly into CellProfiler, a software platform designed for high-throughput image analysis. To facilitate neural network processing, each image was converted into a NumPy array using Python 3 and the Python Imaging Library (PIL). The data set is available for download in five separate ZIP archives, Kuramochi_BjhTERT.zip (93.68 GB), Kuramochi_WI38.zip (93.50 GB), MH_BjhTERT.zip (62.22 GB), OvCar3_BjhTERT.zip (84.98 GB), OvCar8_WI38.zip (91.89 GB). For a description on the file structure, see associated documentation file Dataset_Description.pdf. Detta dataset innehåller detaljerade bilddata från olika kokulturanalyser av äggstockscancer- och fibroblastcellinjer, behandlade med ett brett utbud av läkemedel. Se utförligare beskrivning i den engelska versionen av katalogposten. Datasetet är tillgängligt för nedladdning i fem separata ZIP-arkiv: Kuramochi_BjhTERT.zip, (93,68 GB), Kuramochi_WI38.zip (93,50 GB), MH_BjhTERT.zip (62,22 GB), OvCar3_BjhTERT.zip (84,98 GB), OvCar8_WI38.zip (91,89 GB). Filstrukturen finns beskriven i den tillhörande dokumentationsfilen Dataset_Description.pdf. CellsCells CellerCeller This dataset consists of images of ovarian cancer cell lines and fibroblasts cell lines grown together (2D coculture) in a 384 well microtiter plate (in vitro). In our analysis each single cell is an object. These cocultures are used to study how cancer and fibroblasts cells interact with each other and upon drug purturbation and how the morphology of cancer cells changes. Datasetet består av bilder av äggstockscancercellinjer och fibroblastcellinjer som odlats tillsammans (2D-kokultur) i en 384-brunnars mikrotiterplatta (in vitro). I vår analys är varje enskild cell ett objekt. Dessa samodlingar används för att studera hur cancer- och fibroblastceller interagerar med varandra och vid läkemedelspåverkan samt hur cancercellernas morfologi förändras Still image OtherOther ÖvrigtÖvrigt OtherOther ÖvrigtÖvrigt Access to data through SND. Data are freely accessible. Åtkomst till data via SND. Data är fritt tillgängliga. openAccess Sharma, O., Gudoityte, G., Minozada, R., Kallioniemi, O., Turkki, R., Paavolainen, L., & Seashore-Ludlow, B. (2025). Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks. In COMMUNICATIONS BIOLOGY (Vol. 8, Issue 1). https://doi.org/10.1038/s42003-025-07766-w Sharma, O., Gudoityte, G., Minozada, R., Kallioniemi, O., Turkki, R., Paavolainen, L., & Seashore-Ludlow, B. (2025). Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks. In COMMUNICATIONS BIOLOGY (Vol. 8, Issue 1). https://doi.org/10.1038/s42003-025-07766-w 10.1038/s42003-025-07766-w oai:swepub.ki.se:906885 2025 oai:swepub.ki.se:906885