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BlobFinder, a tool for fluorescence microscopy image cytometry
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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2009 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 94, no 1, p. 58-65Article in journal (Refereed) Published
Abstract [en]

Images can be acquired at high rates with modern fluorescence microscopy hardware, giving rise to a demand for high-speed analysis of image data. Digital image cytometry, i.e., automated measurements and extraction of quantitative data from images of cells, provides valuable information for many types of biomedical analysis. There exists a number of different image analysis software packages that can be programmed to perform a wide array of useful measurements. However, the multi-application capability often compromises the simplicity of the tool. Also, the gain in speed of analysis is often compromised by time spent learning complicated software. We provide a free software called BlobFinder that is intended for a limited type of application, making it easy to use, easy to learn and optimized for its particular task. BlobFinder can perform batch processing of image data and quantify as well as localize cells and point like source signals in fluorescence microscopy images, e.g., from FISH, in situ PLA and padlock probing, in a fast and easy way.

Place, publisher, year, edition, pages
2009. Vol. 94, no 1, p. 58-65
Keywords [en]
Image cytometry, Single cell analysis, FISH, Software
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-87971DOI: 10.1016/j.cmpb.2008.08.006ISI: 000264282400006PubMedID: 18950895OAI: oai:DiVA.org:uu-87971DiVA, id: diva2:134039
Available from: 2009-01-22 Created: 2009-01-16 Last updated: 2018-06-26Bibliographically approved
In thesis
1. Methods for 2D and 3D Quantitative Microscopy of Biological Samples
Open this publication in new window or tab >>Methods for 2D and 3D Quantitative Microscopy of Biological Samples
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening. 

It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that  produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions. 

HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. p. 70
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 856
Keywords
Image analysis, cytmetry, model organism, zebrafish, screening
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-159196 (URN)978-91-554-8167-4 (ISBN)
Public defence
2011-11-11, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2011-10-21 Created: 2011-09-25 Last updated: 2014-07-21Bibliographically approved

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Allalou, AminWählby, Carolina

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