SuperSegger: Robust image segmentation, analysis and lineage tracking of bacterial cells

Research output: Contribution to journalJournal articleResearchpeer-review

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment micro-colonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter, and neighboring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of post-processing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies, and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. This article is protected by copyright. All rights reserved.

Original languageEnglish
JournalMolecular Microbiology
Issue number4
Pages (from-to)690-700
Number of pages11
Publication statusPublished - 11 Nov 2016

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 166064354