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Image Analysis and Deep Learning for Applications in Microscopy
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range from traditional image analysis techniques to use of deep learning architectures.

Many biomedical microscopy assays result in fluorescent spots. Robust detection and precise localization of these spots are two important, albeit sometimes overlapping, areas for application of quantitative image analysis. We demonstrate the use of popular deep learning architectures for spot detection and compare them against more traditional parametric model-based approaches. Moreover, we quantify the effect of pre-training and change in the size of training sets on detection performance. Thereafter, we determine the potential of training deep networks on synthetic and semi-synthetic datasets and their comparison with networks trained on manually annotated real data. In addition, we present a two-alternative forced-choice based tool for assisting in manual annotation of real image data. On a spot localization track, we parallelize a popular compressed sensing based localization method and evaluate its performance in conjunction with different optimizers, noise conditions and spot densities. We investigate its sensitivity to different point spread function estimates.

Zebrafish is an important model organism, attractive for whole-organism image-based assays for drug discovery campaigns. The effect of drug-induced neuronal damage may be expressed in the form of zebrafish shape deformation. First, we present an automated method for accurate quantification of tail deformations in multi-fish micro-plate wells using image analysis techniques such as illumination correction, segmentation, generation of branch-free skeletons of partial tail-segments and their fusion to generate complete tails. Later, we demonstrate the use of a deep learning-based pipeline for classifying micro-plate wells as either drug-affected or negative controls, resulting in competitive performance, and compare the performance from deep learning against that from traditional image analysis approaches. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. , 76 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1371
Keyword [en]
Machine learning, Deep learning, Image analysis, Quantitative microscopy, Bioimaging
National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-283846ISBN: 978-91-554-9567-1 (print)OAI: oai:DiVA.org:uu-283846DiVA: diva2:919645
Public defence
2016-06-09, 2446, ITC, Lägerhyddsvägen 2, Hus 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2016-05-18 Created: 2016-04-14 Last updated: 2016-06-01Bibliographically approved
List of papers
1. An Evaluation of the Faster STORM Method for Super-resolution Microscopy
Open this publication in new window or tab >>An Evaluation of the Faster STORM Method for Super-resolution Microscopy
2014 (English)In: Proceedings of the 22nd International Conference on Pattern Recognition, 2014, 4435-4440 p.Conference paper, Published paper (Refereed)
Abstract [en]

Development of new stochastic super-resolution methods together with fluorescence microscopy imaging enables visualization of biological processes at increasing spatial and temporal resolution. Quantitative evaluation of such imaging experiments call for computational analysis methods that localize the signals with high precision and recall. Furthermore, it is desirable that the methods are fast and possible to parallelize so that the ever increasing amounts of collected data can be handled in an efficient way. We here in address signal detection in super-resolution microscopy by approaches based on compressed sensing. We describe how a previously published approach can be parallelized, reducing processing time at least four times. We also evaluate the effect of a greedy optimization approach on signal recovery at high noise and molecule density. Furthermore, our evaluation reveals how previously published compressed sensing algorithms have a performance that degrades to that of a random signal detector at high molecule density. Finally, we show the approximation of the imaging system's point spread function affects recall and precision of signal detection, illustrating the importance of parameter optimization. We evaluate the methods on synthetic data with varying signal to noise ratio and increasing molecular density, and visualize performance on realsuper-resolution microscopy data from a time-lapse sequence of livingcells.

Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Signal Processing Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-238600 (URN)10.1109/ICPR.2014.759 (DOI)000359818004096 ()978-1-4799-5208-3 (ISBN)
Conference
22nd International Conference on Pattern Recognition, 24-28 August, 2014S, tockholm, Sweden
Available from: 2014-12-14 Created: 2014-12-14 Last updated: 2016-05-20Bibliographically approved
2. Evaluation of Deep Learning for Detection of Fluorescent Spots in Real Data
Open this publication in new window or tab >>Evaluation of Deep Learning for Detection of Fluorescent Spots in Real Data
(English)Manuscript (preprint) (Other academic)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-283731 (URN)
Available from: 2016-04-14 Created: 2016-04-14 Last updated: 2016-05-20
3. Training of Machine Learning Methods for Fluorescent Spot Detection
Open this publication in new window or tab >>Training of Machine Learning Methods for Fluorescent Spot Detection
(English)Article in journal (Refereed) Submitted
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-283736 (URN)
Available from: 2016-04-14 Created: 2016-04-14 Last updated: 2016-05-20
4. Compaction of rolling circle amplification products increases signal integrity and signal–to–noise ratio
Open this publication in new window or tab >>Compaction of rolling circle amplification products increases signal integrity and signal–to–noise ratio
Show others...
2015 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, 12317:1-10 p., 12317Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-260286 (URN)10.1038/srep12317 (DOI)000358358900001 ()26202090 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, 278568EU, FP7, Seventh Framework Programme, 259796Swedish Research Council
Available from: 2015-07-23 Created: 2015-08-18 Last updated: 2017-12-04Bibliographically approved
5. Bridging Histology and Bioinformatics: Computational analysis of spatially resolved transcriptomics
Open this publication in new window or tab >>Bridging Histology and Bioinformatics: Computational analysis of spatially resolved transcriptomics
2017 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 105, no 3, 530-541 p.Article in journal (Refereed) Published
Abstract [en]

It is well known that cells in tissue display a large heterogeneity in gene expression due to differences in cell lineage origin and variation in the local environment. Traditional methods that analyze gene expression from bulk RNA extracts fail to accurately describe this heterogeneity because of their intrinsic limitation in cellular and spatial resolution. Also, information on histology in the form of tissue architecture and organization is lost in the process. Recently, new transcriptome-wide analysis technologies have enabled the study of RNA molecules directly in tissue samples, thus maintaining spatial resolution and complementing histological information with molecular information important for the understanding of many biological processes and potentially relevant for the clinical management of cancer patients. These new methods generally comprise three levels of analysis. At the first level, biochemical techniques are used to generate signals that can be imaged by different means of fluorescence microscopy. At the second level, images are subject to digital image processing and analysis in order to detect and identify the aforementioned signals. At the third level, the collected data are analyzed and transformed into interpretable information by statistical methods and visualization techniques relating them to each other, to spatial distribution, and to tissue morphology. In this review, we describe state-of-the-art techniques used at all three levels of analysis. Finally, we discuss future perspective in this fast-growing field of spatially resolved transcriptomics.

Keyword
Biomedical image processing, biomedical signal analysis, computer-aided analysis, genetics, image analysis, image processing
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-283723 (URN)10.1109/JPROC.2016.2538562 (DOI)000395894900011 ()
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceeSSENCE - An eScience CollaborationSwedish Research Council, 2012-4968 2014-00599
Available from: 2016-04-06 Created: 2016-04-14 Last updated: 2017-04-27Bibliographically approved
6. Automated quantification of Zebrafish tail deformation for high-throughput drug screening
Open this publication in new window or tab >>Automated quantification of Zebrafish tail deformation for high-throughput drug screening
Show others...
2013 (English)In: Proc. 10th International Symposium on Biomedical Imaging: From Nano to Macro, Piscataway, NJ: IEEE , 2013, 902-905 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2013
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-215457 (URN)10.1109/ISBI.2013.6556621 (DOI)000326900100226 ()978-1-4673-6456-0 (ISBN)
Conference
ISBI 2013, April 7-11, San Francisco, CA
Available from: 2013-04-11 Created: 2014-01-14 Last updated: 2016-05-20Bibliographically approved
7. Deep Fish: Deep Learning-based Classification of Zebrafish Deformation for High-throughput Screening
Open this publication in new window or tab >>Deep Fish: Deep Learning-based Classification of Zebrafish Deformation for High-throughput Screening
(English)Manuscript (preprint) (Other academic)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-283738 (URN)
Available from: 2016-04-14 Created: 2016-04-14 Last updated: 2016-05-20

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