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An annotated high-c...
An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines : Dataset record
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- Arvidsson, Malou (författare)
- Lund University,Cell Death, Lysosomes and Artificial Intelligence
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- Kazemi Rashed, Salma (författare)
- Lund University,Lunds universitet,Celldöd, Lysosomer och Artificiell Intelligens,Forskargrupper vid Lunds universitet,Cell Death, Lysosomes and Artificial Intelligence,Lund University Research Groups
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- Aits, Sonja (författare)
- 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)
- 2022
- Engelska.
- Relaterad länk:
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http://dx.doi.org/10... (free)
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visa fler...
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https://lup.lub.lu.s...
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https://doi.org/10.5...
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Abstract
Ämnesord
Stäng
- 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)
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Medical Genetics (hsv//eng)
Publikations- och innehållstyp
- ovr (ämneskategori)
- vet (ämneskategori)