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An Evaluation of the Faster STORM Method for Super-resolution 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. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
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, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
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.

Place, publisher, year, edition, pages
2014. 4435-4440 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Signal Processing Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-238600DOI: 10.1109/ICPR.2014.759ISI: 000359818004096ISBN: 978-1-4799-5208-3 (print)OAI: oai:DiVA.org:uu-238600DiVA: diva2:771539
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
In thesis
1. Image Analysis and Deep Learning for Applications in Microscopy
Open this publication in new window or tab >>Image Analysis and Deep Learning for Applications in Microscopy
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
Machine learning, Deep learning, Image analysis, Quantitative microscopy, Bioimaging
National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-283846 (URN)978-91-554-9567-1 (ISBN)
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

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Ishaq, OmerElf, JohanWählby, Carolina

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