An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines : Dataset record

Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert.The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A preprint of a brief article describing the dataset is also available from zenodo (Arvidsson M, Kazemi Rashed S, Aits S. zenodo 2022, https://doi.org/10.1016/j.dib.2022.108769

An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines : Dataset record

Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert.The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A preprint of a brief article describing the dataset is also available from zenodo (Arvidsson M, Kazemi Rashed S, Aits S. zenodo 2022, https://doi.org/10.1016/j.dib.2022.108769