SuperSegger: Robust image segmentation, analysis and lineage tracking of bacterial cells
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SuperSegger : Robust image segmentation, analysis and lineage tracking of bacterial cells. / Stylianidou, Stella; Brennan, Connor; Nissen, Silas B; Kuwada, Nathan J; Wiggins, Paul A.
I: Molecular Microbiology, Bind 102, Nr. 4, 11.11.2016, s. 690-700.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - SuperSegger
T2 - Robust image segmentation, analysis and lineage tracking of bacterial cells
AU - Stylianidou, Stella
AU - Brennan, Connor
AU - Nissen, Silas B
AU - Kuwada, Nathan J
AU - Wiggins, Paul A
N1 - © 2016 John Wiley & Sons Ltd.
PY - 2016/11/11
Y1 - 2016/11/11
N2 - 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.
AB - 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.
U2 - 10.1111/mmi.13486
DO - 10.1111/mmi.13486
M3 - Journal article
C2 - 27569113
VL - 102
SP - 690
EP - 700
JO - Molecular Microbiology
JF - Molecular Microbiology
SN - 0950-382X
IS - 4
ER -
ID: 166064354