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Algorithms for cytoplasm segmentation of fluorescence labeled cells
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
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2002 (English)In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 24, no 2-3, 101-111 p.Article in journal (Refereed) Published
Abstract [en]

Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

Place, publisher, year, edition, pages
2002. Vol. 24, no 2-3, 101-111 p.
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-90086PubMedID: 12446959OAI: oai:DiVA.org:uu-90086DiVA: diva2:162264
Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Development of Algorithms for Digital Image Cytometry
Open this publication in new window or tab >>Development of Algorithms for Digital Image Cytometry
2002 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents work in digital image cytometry applied to fluorescence microscope images of cultivated cells. Focus has been on the development and compilation of robust image analysis tools, enabling quantitative measurements of various properties of cells and cell structures. A significant part of the work has consisted of developing robust segmentation methods for fluorescently labelled cells. This, in combination with effort applied in the areas of feature extraction and statistical data analysis, has enabled the compilation of a complete chain of processing steps to produce a system capable of performing fully automatic segmentation and classification of fluorescently labelled cells according to their level of activation.

Two sequences of processing steps, both leading to automatic cytoplasm segmentation of fluorescence microscopy cell images are presented. In one of the sequences, an additional image of the nuclei of the cells is segmented. The nuclei are then used as seeds for the segmentation of the cytoplasm image. This solves the problem of over-segmentation of the cytoplasms in an efficient way. The other sequence uses merge and split algorithms on the cytoplasm image, in conjunction with statistical analysis of descriptive features. This analysis is used in a feedback system to improve the segmentation performance, and to give an overall quality measure of the segmentation.

A classification method that separates individual cells into three classes, depending on their level of activation, is described. The method is based on analysis of time series of images. Using both general purpose features and carefully designed problem specific features, in combination with a floating feature selection procedure, a Bayesian classifier is built. Evaluation showed that the performance of the fully automatic classification procedure was very close to the performance of skilled manual classification.

A novel method for performing estimation of intensity nonuniformites of microscope images is presented. Methods to solve many other problems related to image analysis of cell images are discussed and evaluated. All methods presented in this work are applicable to real-world situations. The two main projects of the thesis work have been performed in close cooperation with and according to demands of the biomedical industry.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2002. 67 p.
Series
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 789
Keyword
Bildanalys, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-3239 (URN)91-554-5497-6 (ISBN)
Public defence
2003-01-17, Häggsalen (room 10132), Ångströmlaboratoriet, Uppsala, 10:15
Opponent
Available from: 2002-12-19 Created: 2002-12-19Bibliographically approved
2. Algorithms for Applied Digital Image Cytometry
Open this publication in new window or tab >>Algorithms for Applied Digital Image Cytometry
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Image analysis can provide genetic as well as protein level information from fluorescence stained fixed or living cells without loosing tissue morphology. Analysis of spatial, spectral, and temporal distribution of fluorescence can reveal important information on the single cell level. This is in contrast to most other methods for cell analysis, which do not account for inter-cellular variation. Flow cytometry enables single-cell analysis, but tissue morphology is lost in the process, and temporal events cannot be observed.

The need for reproducibility, speed and accuracy calls for computerized methods for cell image analysis, i.e., digital image cytometry, which is the topic of this thesis.

Algorithms for cell-based screening are presented and applied to evaluate the effect of insulin on translocation events in single cells. This type of algorithms could be the basis for high-throughput drug screening systems, and have been developed in close cooperation with biomedical industry.

Image based studies of cell cycle proteins in cultured cells and tissue sections show that cyclin A has a well preserved expression pattern while the expression pattern of cyclin E is disturbed in tumors. The results indicate that analysis of cyclin E expression provides additional valuable information for cancer prognosis, not visible by standard tumor grading techniques.

Complex chains of events and interactions can be visualized by simultaneous staining of different proteins involved in a process. A combination of image analysis and staining procedures that allow sequential staining and visualization of large numbers of different antigens in single cells is presented. Preliminary results show that at least six different antigens can be stained in the same set of cells.

All image cytometry requires robust segmentation techniques. Clustered objects, background variation, as well as internal intensity variations complicate the segmentation of cells in tissue. Algorithms for segmentation of 2D and 3D images of cell nuclei in tissue by combining intensity, shape, and gradient information are presented.

The algorithms and applications presented show that fast, robust, and automatic digital image cytometry can increase the throughput and power of image based single cell analysis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2003. 73 p.
Series
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 896
Keyword
Bildanalys, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-3608 (URN)91-554-5759-2 (ISBN)
Public defence
2003-10-31, Häggsalen (room 10132), Ångström Laboratory, Uppsala, 10:15
Opponent
Supervisors
Available from: 2003-10-09 Created: 2003-10-09 Last updated: 2016-01-18Bibliographically approved

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