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An annotated high-c...
An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines
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- Arvidsson, Malou (author)
- Lund University
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- Rashed, Salma Kazemi (author)
- Lund University,Lunds universitet,Celldöd, Lysosomer och Artificiell Intelligens,Forskargrupper vid Lunds universitet,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Cell Death, Lysosomes and Artificial Intelligence,Lund University Research Groups,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas
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- Aits, Sonja (author)
- Lund University,Lunds universitet,Celldöd, Lysosomer och Artificiell Intelligens,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,LTH profilområde: Teknik för hälsa,Cell Death, Lysosomes and Artificial Intelligence,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- English.
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In: Data in Brief. - : Elsevier BV. - 2352-3409. ; 46
- Related links:
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http://dx.doi.org/10... (free)
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Abstract
Subject headings
Close
- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- Biomedical image analysis
- Computer vision
- Deep learning training and evaluation
- Fluorescence microscopy
- High-content screening
- Instance segmentation
Publication and Content Type
- art (subject category)
- ref (subject category)
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