Segmentation of cell structures in fluorescence confocal microscopy images

During the past several years, image segmentation techniques have been developed and extensively used in biomedical applications as an important tool to extract objects and boundaries of interest. In biological field, cytoskeleton analysis is a complicated problem and the analysing technique is still immature. Cytoskeleton plays an important role in normal cell activities, including motion and division, which make the cell cytoskeleton important to investigate. The objective of this project is to investigate and evaluate level set segmentation methods for segmentation of both cell nuclei and membrane segmentation of microfilament images captured by fluorescent confocal microscopy. Based on some background investigations, the active contour methodology has been selected as the fundamental method for image segmentation. This thesis presents the methods used and reports on the results achieved for cell and nuclei segmentation using the hybrid level-set method and cell membrane segmentation using the subjective surfaces model. In addition, some initial results of nuclei segmentation in 3-D case based on the hybrid method will be presented as well. Also included in this thesis are the method and the initial categorisation of microtubule images based on the multi-template method. At the end of the thesis, possible directions for potential future work are presented. It is envisaged that the segmentation tools produced by the project will make cell cytoskeleton data analysis much more convenient. In particular, the segmentation of cell membranes will help biologists to perform quantitative analysis of fluorescent confocal microscopy images by measuring the cell properties. With more useful information of cytoskeleton being provided, the work contained in this thesis has the potential to contribute to evaluation and prediction of the possibility of cell canceration

Accurate cell segmentation in microscopy images using membrane patterns

Motivation: Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries. However, many microscopy protocols can generate images with characteristic intensity profiles at the cell membrane. This has not yet been algorithmically exploited to establish more general segmentation methods. Results: We present an automatic cell segmentation method that decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques. In quantitative benchmarks and comparisons with established methods on synthetic and real images, we demonstrate significantly improved segmentation performance despite cell-shape irregularity, cell-to-cell variability and image noise. As a proof of concept, we monitor the internalization of green fluorescent protein-tagged plasma membrane transporters in single yeast cells. Availability and implementation: Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Active Contours and Image Segmentation: The Current State Of the Art

Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours

Toward a morphodynamic model of the cell: Signal processing for cell modeling

From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)

Accumulative Difference Image Protocol for Particle Tracking in Fluorescence Microscopy Tested in Mouse Lymphonodes

The basic research in cell biology and in medical sciences makes large use of imaging tools mainly based on confocal fluorescence and, more recently, on non-linear excitation microscopy. Substantially the aim is the recognition of selected targets in the image and their tracking in time. We have developed a particle tracking algorithm optimized for low signal/noise images with a minimum set of requirements on the target size and with no a priori knowledge of the type of motion. The image segmentation, based on a combination of size sensitive filters, does not rely on edge detection and is tailored for targets acquired at low resolution as in most of the in-vivo studies. The particle tracking is performed by building, from a stack of Accumulative Difference Images, a single 2D image in which the motion of the whole set of the particles is coded in time by a color level. This algorithm, tested here on solid-lipid nanoparticles diffusing within cells and on lymphocytes diffusing in lymphonodes, appears to be particularly useful for the cellular and the in-vivo microscopy image processing in which few a priori assumption on the type, the extent and the variability of particle motions, can be done

Characterising live cell behaviour: traditional label-free and quantitative phase imaging approaches

Label-free imaging uses inherent contrast mechanisms within cells to create image contrast without introducing dyes/labels, which may confound results. Quantitative phase imaging is label-free and offers higher content and contrast compared to traditional techniques. High-contrast images facilitate generation of individual cell metrics via more robust segmentation and tracking, enabling formation of a label-free dynamic phenotype describing cell-to-cell heterogeneity and temporal changes. Compared to population-level averages, individual cell-level dynamic phenotypes have greater power to differentiate between cellular responses to treatments, which has clinical relevance e.g. in the treatment of cancer. Furthermore, as the data is obtained label-free, the same cells can be used for further assays or expansion, of potential benefit for the fields of regenerative and personalised medicine

Globally Optimal Cell Tracking using Integer Programming

We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

Characterising live cell behaviour: traditional label-free and quantitative phase imaging approaches

Label-free imaging uses inherent contrast mechanisms within cells to create image contrast without introducing dyes/labels, which may confound results. Quantitative phase imaging is label-free and offers higher content and contrast compared to traditional techniques. High-contrast images facilitate generation of individual cell metrics via more robust segmentation and tracking, enabling formation of a label-free dynamic phenotype describing cell-to-cell heterogeneity and temporal changes. Compared to population-level averages, individual cell-level dynamic phenotypes have greater power to differentiate between cellular responses to treatments, which has clinical relevance e.g. in the treatment of cancer. Furthermore, as the data is obtained label-free, the same cells can be used for further assays or expansion, of potential benefit for the fields of regenerative and personalised medicine