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

Arvidsson, Malou ; Kazemi Rashed, Salma LU and Aits, Sonja LU orcid (2022)
Abstract
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... (More)
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) (Less)
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organization
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publication status
published
subject
publisher
Zenodo
DOI
10.5281/zenodo.6657260
project
Lund University AI Research
Analysing large-scale microscopy datasets with AI-based approaches
Using artificial intelligence and advanced computational tools to identify biological pathways, disease mechanisms and therapeutic opportunities
Environmental toxins as cause of lysosomal damage-induced cell death in animals and humans
Lysosomes in cell death - from molecular mechanisms to new treatment strategies
language
English
LU publication?
yes
id
bad47534-4c9f-4122-93a9-eb80b1c77c91
date added to LUP
2023-01-07 12:27:58
date last changed
2023-01-10 02:44:18
@misc{bad47534-4c9f-4122-93a9-eb80b1c77c91,
  abstract     = {{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.<br/><br/>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)}},
  author       = {{Arvidsson, Malou and Kazemi Rashed, Salma and Aits, Sonja}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Zenodo}},
  title        = {{An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines : Dataset record}},
  url          = {{http://dx.doi.org/10.5281/zenodo.6657260}},
  doi          = {{10.5281/zenodo.6657260}},
  year         = {{2022}},
}