Enhanced segmentation of label‐free cells for automated migration and interaction tracking. Issue 12 (10th June 2021)

• Record Type: Journal Article
• Authors: Belyaev, Ivan; Praetorius, Jan‐Philipp; Medyukhina, Anna; Figge, Marc Thilo
• Other Names: Moore Jonni guestEditor.; Tárnok Attila guestEditor.
• Is Part Of: Cytometry. Volume 99:Issue 12(2021)
• Page Start: 1218
• Page End: 1229
• Publication Date: 2021-06-10
• Subjects: image processing -- label‐free imaging -- segmentation -- tracking; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.24466
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: In biomedical research, the migration behavior of cells and interactions between various cell types are frequently studied subjects. An automated and quantitative analysis of time‐lapse microscopy data is an essential component of these studies, especially when characteristic migration patterns need to be identified. Plenty of software tools have been developed to serve this need. However, the majority of algorithms is designed for fluorescently labeled cells, even though it is well‐known that fluorescent labels can substantially interfere with the physiological behavior of interacting cells. We here present a fully revised version of our algorithm for migration and interaction tracking (AMIT), which includes a novel segmentation approach. This approach allows segmenting label‐free cells with high accuracy and also enables detecting almost all cells within the field of view. With regard to cell tracking, we designed and implemented a new method for cluster detection and splitting. This method does not rely on any geometrical characteristics of individual objects inside a cluster but relies on monitoring the events of cell–cell fusion from and cluster fission into single cells forward and backward in time. We demonstrate that focusing on these events provides accurate splitting of transient clusters. Furthermore, the substantially improved quantitative analysis of cell migration by the revised version of AMIT is more than two orders of magnitude faster than the previous implementation, which makes it feasible to process video data at higher spatial and temporal resolutions.