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DSeg: a dynamic image segmentation program to extract backbone patterns for filamentous bacteria and hyphae structures
Umeå University, Faculty of Science and Technology, Department of Physics. (Biophysics & Biophotonics group)
Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). (Research group Linda Sandblad)ORCID iD: 0000-0003-3964-3751
Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
Umeå University, Faculty of Science and Technology, Department of Physics.
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2019 (English)In: Microscopy and Microanalysis, ISSN 1431-9276, E-ISSN 1435-8115, Vol. 25, no 3, p. 711-719Article in journal (Refereed) Published
Abstract [en]

Analysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.

Place, publisher, year, edition, pages
Cambridge University Press, 2019. Vol. 25, no 3, p. 711-719
Keywords [en]
filamentous, hyphae, image segmentation, MATLAB, software, quantitative measurement
National Category
Biophysics
Research subject
Computerized Image Analysis; cell research
Identifiers
URN: urn:nbn:se:umu:diva-150686DOI: 10.1017/S1431927619000308ISI: 000474798800016PubMedID: 30894244OAI: oai:DiVA.org:umu-150686DiVA, id: diva2:1239194
Note

Originally included in thesis in manuscript form.

The program is available at https://sourceforge.net/projects/dseg-software

Available from: 2018-08-15 Created: 2018-08-15 Last updated: 2019-08-07Bibliographically approved
In thesis
1. Digital holography and image processing methods for applications in biophysics
Open this publication in new window or tab >>Digital holography and image processing methods for applications in biophysics
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Understanding dynamic mechanisms, morphology and behavior of bacteria are important to develop new therapeutics to cure diseases. For example, bacterial adhesion mechanisms are prerequisites for initiation of infections and for several bacterial strains this adhesion process is mediated by adhesive surface organelles, also known as fimbriae. Escherichia coli (E. coli) is a bacterium expressing fimbriae of which pathogenic strains can cause severe diseases in fluidic environments such as the urinary tract and intestine. To better understand how E. coli cells attach and remain attached to surfaces when exposed to a fluid flow using their fimbriae, experiments using microfluidic channels are important; and to assess quantitative information of the adhesion process and cellular information of morphology, location and orientation, the imaging capability of the experimental technique is vital.

In-line digital holographic microscopy (DHM) is a powerful imaging technique that can be realized around a conventional light microscope. It is a non-invasive technique without the need of staining or sectioning of the sample to be observed in vitro. DHM provides holograms containing three-dimensional (3D) intensity and phase information of cells under study with high temporal and spatial resolution. By applying image processing algorithms to the holograms, quantitative measurements can provide information of position, shape, orientation, optical thickness of the cell, as well as dynamic cell properties such as speed, growing rate, etc.

In this thesis, we aim to improve the DHM technique and develop image processing methods to track and assess cellular properties in microfluidic channels to shed light on bacterial adhesion and cell morphology. To achieve this, we implemented a DHM technique and developed image processing algorithms to provide for a robust and quantitative analysis of holograms. We improved the cell detection accuracy and efficiency in DHM holograms by developing an algorithm for detection of cell diffraction patterns. To improve the 3D detection accuracy using in-line digital holography, we developed a novel iterative algorithm that use multiple-wavelengths. We verified our algorithms using synthetic, colloidal and cell data and applied the algorithms for detecting, tracking and analysis. We demonstrated the performance when tracking bacteria with sub-micrometer accuracy and kHz temporal resolution, as well as how DHM can be used to profile a microfluidic flow using a large number of colloidal particles. We also demonstrated how the results of cell shape analysis based on image segmentation can be used to estimate the hydrodynamic force on tethered capsule-shaped cells in micro-fluidic flows near a surface.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 59
Keywords
Digital holographic microscopy, image processing, image reconstruction, bacterial adhesion, cell morphology, algorithm development, software design, quantitative measurement, microfluidics, multidisciplinary research
National Category
Biophysics Computer Vision and Robotics (Autonomous Systems)
Research subject
Signal Processing; Technical Physics
Identifiers
urn:nbn:se:umu:diva-150687 (URN)978-91-7601-915-3 (ISBN)
Public defence
2018-09-07, Naturvetarhuset, N430, Umeå, 13:15 (English)
Opponent
Supervisors
Available from: 2018-08-17 Created: 2018-08-15 Last updated: 2018-08-16Bibliographically approved

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Zhang, HanqingSöderholm, NiklasSandblad, LindaWiklund, KristerAndersson, Magnus

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