| Literature DB >> 31432490 |
Heather S Deter1, Marta Dies2, Courtney C Cameron1, Nicholas C Butzin3, Javier Buceta4,5.
Abstract
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.Entities:
Keywords: Bacterial growth; Cell lineage analysis; Cell segmentation; Cell tracking; Computational image analysis; Fluorescence microscopy; Machine learning; Single-cell quantification
Mesh：
Year: 2019 PMID： 31432490 DOI： 10.1007/978-1-4939-9686-5_19
Source DB: PubMed Journal: Methods Mol Biol ISSN： 1064-3745