Image and Data Analysis for Biomedical Quantitative Microscopy

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Fältnamn	Indikatorer	Metadata
000		05103nam a2200445 4500
001		oai:DiVA.org:uu-394709
003		SwePub
008		191009s2019 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
020		a 9789151307718q print
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3947092 URI
040		a (SwePub)uu
041		a engb eng
042		9 SwePub
072	7	a vet2 swepub-contenttype
072	7	a dok2 swepub-publicationtype
100	1	a Matuszewski, Damian J.,d 1988-u Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion4 aut0 (Swepub:uu)damma800
245	1 0	a Image and Data Analysis for Biomedical Quantitative Microscopy
264	1	a Uppsala :b Acta Universitatis Upsaliensis,c 2019
300		a 63 s.
338		a electronic2 rdacarrier
490	0	a Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology,x 1651-6214 ;v 1864
520		a 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.
650	7	a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng
653		a high-content screening
653		a drug selection
653		a DNA content histogram
653		a manual image annotation
653		a deep learning
653		a convolutional neural networks
653		a hardware integration
653		a Computerized Image Processing
653		a Datoriserad bildbehandling
700	1	a Sintorn, Ida-Maria,c Docent,d 1976-u Uppsala universitet,Avdelningen för visuell information och interaktion4 ths0 (Swepub:uu)idsin102
700	1	a Wählby, Carolina,c Professor,d 1974-u Uppsala universitet,Avdelningen för visuell information och interaktion4 ths0 (Swepub:uu)cli05194
700	1	a Horvath, Peter,c Professoru Institute for Molecular Medicine Finland, University of Helsinki; Institute of Biochemistry, Biological Research Center, Hungarian Academy of Sciences4 opn
710	2	a Uppsala universitetb Avdelningen för visuell information och interaktion4 org
856	4	u https://uu.diva-portal.org/smash/get/diva2:1359483/FULLTEXT01.pdfx primaryx Raw objecty fulltext
856	4	u https://uu.diva-portal.org/smash/get/diva2:1359483/PREVIEW01.jpgx Previewy preview image
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-394709

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