Hinweise zu Qualitätskriterien von Zeitschriften
Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016
CC Licencences
Metriken für Publikationen
Startseite
Login
English
Neuer EVA Antrag
Erweiterte Suche
Durchblättern nach ...
Publikationen im Überblick
HGF Fortschrittsbericht
OA Publikationen
Neue Publikation eintragen
Neue Publikation holen aus...
Fehlende Publikation melden
Ansprechpartner
Hilfe
Bibliometrische Indikatoren
Text
Endnote (RIS)
BibTeX
Verlagsversion
DOI
 |
Open Access Gold |
|
möglich sobald bei der ZB eingereicht worden ist.
microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.
PLoS ONE 17:e0277601 (2022)
In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.
Impact Factor
Scopus SNIP
Altmetric
3.752
0.000
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Bacterial
Sprache
englisch
Veröffentlichungsjahr
2022
HGF-Berichtsjahr
2022
ISSN (print) / ISBN
1932-6203
Zeitschrift
PLoS ONE
Quellenangaben
Band: 17,
Heft: 11,
Artikelnummer: e0277601
Verlag
Public Library of Science (PLoS)
Verlagsort
Lawrence, Kan.
Begutachtungsstatus
Peer reviewed
Institut(e)
Helmholtz AI - KIT (HAI - KIT)
Helmholtz AI - FZJ (HAI - FZJ)
Helmholtz AI - FZJ (HAI - FZJ)
Förderungen
KIT-Publication Fund of the Karlsruhe Institute of Technology
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Helmholtz Imaging Platform within the project SATOMI
Helmholtz Association Initiative and Networking Funds through Helmholtz AI
HIDSS4Health - the Helmholtz Information & Data Science School for Health
Engineering Digital Futures: Supercomputing, Data Management and Information Security for Knowledge and Action
Helmholtz Association in the programs Natural, Artificial and Cognitive Information Processing
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Helmholtz Imaging Platform within the project SATOMI
Helmholtz Association Initiative and Networking Funds through Helmholtz AI
HIDSS4Health - the Helmholtz Information & Data Science School for Health
Engineering Digital Futures: Supercomputing, Data Management and Information Security for Knowledge and Action
Helmholtz Association in the programs Natural, Artificial and Cognitive Information Processing
WOS ID
000925705300023
Scopus ID
85143088482
Erfassungsdatum
2022-12-13