An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines
(2023) In Data in Brief 46.- Abstract
Automated detection of cell nuclei in fluorescence microscopy images is a key task in bioimage analysis. It is essential for most types of microscopy-based high-throughput drug and genomic screening and is often required in smaller scale experiments as well. To develop and evaluate algorithms and neural networks that perform instance or semantic segmentation for detecting nuclei, high quality annotated data is essential. 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... (More)
Automated detection of cell nuclei in fluorescence microscopy images is a key task in bioimage analysis. It is essential for most types of microscopy-based high-throughput drug and genomic screening and is often required in smaller scale experiments as well. To develop and evaluate algorithms and neural networks that perform instance or semantic segmentation for detecting nuclei, high quality annotated data is essential. 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, called Aitslab-bioimaging1, 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. The dataset is split into training, development and test set for user convenience.
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- author
- Arvidsson, Malou ; Rashed, Salma Kazemi LU and Aits, Sonja LU
- organization
-
- LU Profile Area: Natural and Artificial Cognition
- Cell Death, Lysosomes and Artificial Intelligence (research group)
- LTH Profile Area: Engineering Health
- EpiHealth: Epidemiology for Health
- LTH Profile Area: AI and Digitalization
- LUCC: Lund University Cancer Centre
- eSSENCE: The e-Science Collaboration
- publishing date
- 2023-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biomedical image analysis, Computer vision, Deep learning training and evaluation, Fluorescence microscopy, High-content screening, Instance segmentation
- in
- Data in Brief
- volume
- 46
- article number
- 108769
- publisher
- Elsevier
- external identifiers
-
- pmid:36506804
- scopus:85143768599
- ISSN
- 2352-3409
- DOI
- 10.1016/j.dib.2022.108769
- project
- Lysosomes in cell death - from molecular mechanisms to new treatment strategies
- Understanding and therapeutic targeting of lysosome-dependent cell death
- Lund University AI Research
- Analysing large-scale microscopy datasets with AI-based approaches
- Environmental toxins as cause of lysosomal damage-induced cell death in animals and humans
- Using artificial intelligence and advanced computational tools to identify biological pathways, disease mechanisms and therapeutic opportunities
- language
- English
- LU publication?
- yes
- id
- 4451e64a-c796-461f-9d4d-35fecfe82b82
- date added to LUP
- 2022-12-15 08:38:09
- date last changed
- 2024-07-25 16:10:55
@article{4451e64a-c796-461f-9d4d-35fecfe82b82, abstract = {{<p>Automated detection of cell nuclei in fluorescence microscopy images is a key task in bioimage analysis. It is essential for most types of microscopy-based high-throughput drug and genomic screening and is often required in smaller scale experiments as well. To develop and evaluate algorithms and neural networks that perform instance or semantic segmentation for detecting nuclei, high quality annotated data is essential. 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, called Aitslab-bioimaging1, 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. The dataset is split into training, development and test set for user convenience.</p>}}, author = {{Arvidsson, Malou and Rashed, Salma Kazemi and Aits, Sonja}}, issn = {{2352-3409}}, keywords = {{Biomedical image analysis; Computer vision; Deep learning training and evaluation; Fluorescence microscopy; High-content screening; Instance segmentation}}, language = {{eng}}, publisher = {{Elsevier}}, series = {{Data in Brief}}, title = {{An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines}}, url = {{http://dx.doi.org/10.1016/j.dib.2022.108769}}, doi = {{10.1016/j.dib.2022.108769}}, volume = {{46}}, year = {{2023}}, }