A Cell Segmentation/Tracking Tool Based on Machine Learning.
Bacterial growth
Cell lineage analysis
Cell segmentation
Cell tracking
Computational image analysis
Fluorescence microscopy
Machine learning
Single-cell quantification
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2019
2019
Historique:
entrez:
22
8
2019
pubmed:
23
8
2019
medline:
14
4
2020
Statut:
ppublish
Résumé
The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.
Identifiants
pubmed: 31432490
doi: 10.1007/978-1-4939-9686-5_19
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM