Image and Data Analysis for Biomedical Quantitative Microscopy

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Matuszewski, Damian J.,1988-Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion (författare)

Image and Data Analysis for Biomedical Quantitative Microscopy

BokEngelska2019

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Uppsala :Acta Universitatis Upsaliensis,2019
63 s.
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:uu-394709
ISBN:9789151307718
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-394709URI

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Språk:engelska
Sammanfattning på:engelska

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Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology,1651-6214 ;1864

Anmärkningar

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.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
high-content screening
drug selection
DNA content histogram
manual image annotation
deep learning
convolutional neural networks
hardware integration
Computerized Image Processing
Datoriserad bildbehandling

Biuppslag (personer, institutioner, konferenser, titlar ...)

Sintorn, Ida-Maria,Docent,1976-Uppsala universitet,Avdelningen för visuell information och interaktion(Swepub:uu)idsin102 (preses)
Wählby, Carolina,Professor,1974-Uppsala universitet,Avdelningen för visuell information och interaktion(Swepub:uu)cli05194 (preses)
Horvath, Peter,ProfessorInstitute for Molecular Medicine Finland, University of Helsinki; Institute of Biochemistry, Biological Research Center, Hungarian Academy of Sciences (opponent)
Uppsala universitetAvdelningen för visuell information och interaktion (creator_code:org_t)

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Av författaren/redakt...: Matuszewski, Dam ...; Sintorn, Ida-Mar ...; Wählby, Carolina ...; Horvath, Peter, ...

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik; och Medicinsk bildbe ...

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Av lärosätet: Uppsala universitet

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