An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines
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 ne...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340922009726 |
| _version_ | 1797938139895955456 |
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| author | Malou Arvidsson Salma Kazemi Rashed Sonja Aits |
| author_facet | Malou Arvidsson Salma Kazemi Rashed Sonja Aits |
| author_sort | Malou Arvidsson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T18:55:01Z |
| format | Article |
| id | doaj.art-baa9873d06644636aea66e24f317db5f |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-10T18:55:01Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-baa9873d06644636aea66e24f317db5f2023-02-01T04:25:56ZengElsevierData in Brief2352-34092023-02-0146108769An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlinesMalou Arvidsson0Salma Kazemi Rashed1Sonja Aits2Cell Death, Lysosomes and Artificial Intelligence Group, Department of Experimental Medical Science, Faculty of Medicine, Lund University, SwedenCell Death, Lysosomes and Artificial Intelligence Group, Department of Experimental Medical Science, Faculty of Medicine, Lund University, SwedenCorresponding author.; Cell Death, Lysosomes and Artificial Intelligence Group, Department of Experimental Medical Science, Faculty of Medicine, Lund University, SwedenAutomated 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.http://www.sciencedirect.com/science/article/pii/S2352340922009726Instance segmentationFluorescence microscopyBiomedical image analysisHigh-content screeningComputer visionDeep learning training and evaluation |
| spellingShingle | Malou Arvidsson Salma Kazemi Rashed Sonja Aits An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines Data in Brief Instance segmentation Fluorescence microscopy Biomedical image analysis High-content screening Computer vision Deep learning training and evaluation |
| title | An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines |
| title_full | An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines |
| title_fullStr | An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines |
| title_full_unstemmed | An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines |
| title_short | An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines |
| title_sort | annotated high content fluorescence microscopy dataset with hoechst 33342 stained nuclei and manually labelled outlines |
| topic | Instance segmentation Fluorescence microscopy Biomedical image analysis High-content screening Computer vision Deep learning training and evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2352340922009726 |
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