Academic Journal
Προβολή σε EDS

An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlines

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlines
Συγγραφείς: Rashed, Salma Kazemi, Arvidsson, Malou, Ahmed, Rafsan, Aits, Sonja
Συνεισφορές: Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Engineering Health, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Teknik för hälsa, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator, Lund University, Faculty of Medicine, Department of Experimental Medical Science, Cell Death, Lysosomes and Artificial Intelligence, Lunds universitet, Medicinska fakulteten, Institutionen för experimentell medicinsk vetenskap, Celldöd, Lysosomer och Artificiell Intelligens, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Proactive Ageing, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Proaktivt åldrande, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Nature-based future solutions, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturbaserade framtidslösningar, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EpiHealth: Epidemiology for Health, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EpiHealth: Epidemiology for Health, Originator, Lund University, Profile areas and other strong research environments, Other Strong Research Environments, LUCC: Lund University Cancer Centre, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Övriga starka forskningsmiljöer, LUCC: Lunds universitets cancercentrum, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator
Πηγή: Data in Brief. 58:1-9
Θεματικοί όροι: Natural Sciences, Computer and Information Sciences, Computer graphics and computer vision, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datorgrafik och datorseende
Περιγραφή: Many forms of bioimage analysis involve the detection of objects and their outlines. In the context of microscopy-based high-throughput drug and genomic screening and even in smaller scale microscopy experiments, the objects that most often need to be detected are cells. In order to develop and benchmark algorithms and neural networks that can perform this task, high-quality datasets with annotated cell outlines are needed. We have created a dataset, named Aitslab_bioimaging2, consisting of 60 fluorescence microscopy images with EGFP-Galectin-3 labelled cells and their hand-labelled outlines. Images were acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification created as part of an RNA interference screen with a modified U2OS osteosarcoma cell line. Outlines were labelled by three annotators, who had high inter-annotator agreement between them and with a biomedical expert, who labelled some of the objects for comparison and reviewed a subset of the labels, making minor corrections as needed. The dataset comprises over 2200 annotated cell objects in total, making it sufficient in size to train high-performing neural networks for instance or semantic segmentation. Labels can also easily be converted to boxes for object detection tasks. The dataset is already pre-divided into training, development, and test sets. Matching nuclear staining and outlines are available for part of the dataset from a previous publication (dataset Aitslab_bioimaging1) [1].
Σύνδεσμος πρόσβασης: https://doi.org/10.1016/j.dib.2024.111148
Βάση Δεδομένων: SwePub
Περιγραφή
ISSN:23523409
DOI:10.1016/j.dib.2024.111148