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Comparison of Flow Cytometry and Image-Based Screening for Cell Cycle Analysis
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-6148-5174
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Science for Life Laboratory, SciLifeLab. Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
2016 (English)In: Image Analysis And Recognition (ICIAR 2016) / [ed] Aurélio Campilho, Fakhri Karray, Springer, 2016, Vol. 9730, p. 623-630Conference paper, Published paper (Refereed)
Abstract [en]

Quantitative cell state measurements can provide a wealth of information about mechanism of action of chemical compounds and gene functionality. Here we present a comparison of cell cycle disruption measurements from commonly used flow cytometry (generating onedimensional signal data) and bioimaging (producing two-dimensional image data). Our results show high correlation between the two approaches indicating that image-based screening can be used as an alternative to flow cytometry. Furthermore, we discuss the benefits of image informatics over conventional single-signal flow cytometry.

Place, publisher, year, edition, pages
Springer, 2016. Vol. 9730, p. 623-630
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9730
Keywords [en]
Quantitative microscopy, DNA content histogram
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-307245DOI: 10.1007/978-3-319-41501-7_70ISI: 000386604000070OAI: oai:DiVA.org:uu-307245DiVA, id: diva2:1045954
Conference
13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016
Available from: 2016-11-11 Created: 2016-11-11 Last updated: 2019-10-09Bibliographically approved
In thesis
1. Image and Data Analysis for Biomedical Quantitative Microscopy
Open this publication in new window or tab >>Image and Data Analysis for Biomedical Quantitative Microscopy
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents automatic image and data analysis methods to facilitate and improve microscopy-based research and diagnosis. New technologies and computational tools are necessary for handling the ever-growing amounts of data produced in life science. The thesis presents methods developed in three projects with different biomedical applications.

In the first project, we analyzed a large high-content screen aimed at enabling personalized medicine for glioblastoma patients. We focused on capturing drug-induced cell-cycle disruption in fluorescence microscopy images of cancer cell cultures. Our main objectives were to identify drugs affecting the cell-cycle and to increase the understanding of different drugs’ mechanisms of action.  Here we present tools for automatic cell-cycle analysis and identification of drugs of interest and their effective doses.

In the second project, we developed a feature descriptor for image matching. Image matching is a central pre-processing step in many applications. For example, when two or more images must be matched and registered to create a larger field of view or to analyze differences and changes over time. Our descriptor is rotation-, scale-, and illumination-invariant and it has a short feature vector which makes it computationally attractive. The flexibility to combine it with any feature detector and the customization possibility make it a very versatile tool.

In the third project, we addressed two general problems for bridging the gap between deep learning method development and their use in practical scenarios. We developed a method for convolutional neural network training using minimally annotated images. In many biomedical applications, the objects of interest cannot be accurately delineated due to their fuzzy shape, ambiguous morphology, image quality, or the expert knowledge and time it requires. The minimal annotations, in this case, consist of center-points or centerlines of target objects of approximately known size. We demonstrated our training method in a challenging application of a multi-class semantic segmentation of viruses in transmission electron microscopy images. We also systematically explored the influence of network architecture hyper-parameters on its size and performance and show the possibility to substantially reduce the size of a network without compromising its performance.

All methods in this thesis were designed to work with little or no input from biomedical experts but of course, require fine-tuning for new applications. The usefulness of the tools has been demonstrated by collaborators and other researchers and has inspired further development of related algorithms.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 63
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1864
Keywords
high-content screening, drug selection, DNA content histogram, manual image annotation, deep learning, convolutional neural networks, hardware integration
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-394709 (URN)978-91-513-0771-8 (ISBN)
Public defence
2019-12-12, 2446, ITC, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2019-11-20 Created: 2019-10-09 Last updated: 2019-11-27

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Matuszewski, Damian J.Sintorn, Ida-MariaWählby, Carolina

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Matuszewski, Damian J.Sintorn, Ida-MariaWählby, Carolina
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