Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks - Sharma, Osheen; Seashore-Ludlow, Brinton (Karolinska Institutet, 2025)
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.
Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks
Keywords: neural network; neural network; Ovarian cancer cell lines; Äggstockscancer cellinjer; fibroblast cell lines; fibroblastcellinjer; image analysis; image analysis; Morphology features; Morfologiska egenskaper; mechanism of action; mechanism of action; high content imaging; high content imaging; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); Computer graphics and computer vision; Datorgrafik och datorseende; Cell Biology; Cellbiologi; Bioinformatics and Computational Biology; Bioinformatik och beräkningsbiologi; Pharmacology and Toxicology; Farmakologi och toxikologi; Cancer and Oncology; Cancer och onkologi