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An Evaluation of the Faster STORM Method for Super-resolution Microscopy
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
2014 (Engelska)Ingår i: Proceedings of the 22nd International Conference on Pattern Recognition, 2014, s. 4435-4440Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2014. s. 4435-4440
Serie
International Conference on Pattern Recognition, ISSN 1051-4651
Nationell ämneskategori
Signalbehandling Medicinsk bildbehandling
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-238600DOI: 10.1109/ICPR.2014.759ISI: 000359818004096ISBN: 978-1-4799-5208-3 (tryckt)OAI: oai:DiVA.org:uu-238600DiVA, id: diva2:771539
Konferens
22nd International Conference on Pattern Recognition, 24-28 August, 2014S, tockholm, Sweden
Tillgänglig från: 2014-12-14 Skapad: 2014-12-14 Senast uppdaterad: 2016-05-20Bibliografiskt granskad
Ingår i avhandling
1. Image Analysis and Deep Learning for Applications in Microscopy
Öppna denna publikation i ny flik eller fönster >>Image Analysis and Deep Learning for Applications in Microscopy
2016 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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. 

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2016. s. 76
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1371
Nyckelord
Machine learning, Deep learning, Image analysis, Quantitative microscopy, Bioimaging
Nationell ämneskategori
Signalbehandling
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-283846 (URN)978-91-554-9567-1 (ISBN)
Disputation
2016-06-09, 2446, ITC, Lägerhyddsvägen 2, Hus 2, Uppsala, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2016-05-18 Skapad: 2016-04-14 Senast uppdaterad: 2016-06-01Bibliografiskt granskad

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Ishaq, OmerElf, JohanWählby, Carolina
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Avdelningen för visuell information och interaktionBildanalys och människa-datorinteraktionScience for Life Laboratory, SciLifeLabBeräknings- och systembiologi
SignalbehandlingMedicinsk bildbehandling

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