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Magnusson, Klas E. G.ORCID iD iconorcid.org/0000-0002-5329-575X
Alternative names
Publications (10 of 13) Show all publications
Ulman, V., Magnusson, K. E. G., Jaldén, J., Ortiz-de-Solorzano, C. & et al., . (2017). An objective comparison of cell-tracking algorithms. Nature Methods, 14(12), 1141-+.
Open this publication in new window or tab >>An objective comparison of cell-tracking algorithms
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2017 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 14, no 12, p. 1141-+Article in journal (Refereed) Published
Abstract [en]

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2017
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-221073 (URN)10.1038/nmeth.4473 (DOI)000416604800015 ()29083403 (PubMedID)2-s2.0-85036663036 (Scopus ID)
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-01-11Bibliographically approved
Yajnanarayana, V., Magnusson, K. E. G., Brandt, R., Dwivedi, S. & Händel, P. (2017). Optimal Scheduling for Interference Mitigation by Range Information. IEEE Transactions on Mobile Computing, 16(11), 3167-3181.
Open this publication in new window or tab >>Optimal Scheduling for Interference Mitigation by Range Information
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2017 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 16, no 11, p. 3167-3181Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-216601 (URN)10.1109/TMC.2017.2688417 (DOI)000412231100014 ()2-s2.0-85021809245 (Scopus ID)
Note

QC 20171115

Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2017-11-15Bibliographically approved
Ho, A. T. V., Palla, A. R., Blake, M. R., Yucel, N. D., Wang, Y. X., Magnusson, K. E. G., . . . Blau, H. M. (2017). Prostaglandin E2 is essential for efficacious skeletal muscle stem-cell function, augmenting regeneration and strength. Proceedings of the National Academy of Sciences of the United States of America, 114(26), 6675-6684.
Open this publication in new window or tab >>Prostaglandin E2 is essential for efficacious skeletal muscle stem-cell function, augmenting regeneration and strength
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2017 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 114, no 26, p. 6675-6684Article in journal (Refereed) Published
Abstract [en]

Skeletal muscles harbor quiescent muscle-specific stem cells (MuSCs) capable of tissue regeneration throughout life. Muscle injury precipitates a complex inflammatory response in which a multiplicity of cell types, cytokines, and growth factors participate. Here we show that Prostaglandin E2 (PGE2) is an inflammatory cytokine that directly targets MuSCs via the EP4 receptor, leading to MuSC expansion. An acute treatment with PGE2 suffices to robustly augment muscle regeneration by either endogenous or transplanted MuSCs. Loss of PGE2 signaling by specific genetic ablation of the EP4 receptor in MuSCs impairs regeneration, leading to decreased muscle force. Inhibition of PGE2 production through nonsteroidal anti-inflammatory drug (NSAID) administration just after injury similarly hinders regeneration and compromises muscle strength. Mechanistically, the PGE2 EP4 interaction causes MuSC expansion by triggering a cAMP/phosphoCREB pathway that activates the proliferation-inducing transcription factor, Nurr1. Our findings reveal that loss of PGE2 signaling to MuSCs during recovery from injury impedes muscle repair and strength. Through such gain-or loss-of-function experiments, we found that PGE2 signaling acts as a rheostat for muscle stem-cell function. Decreased PGE2 signaling due to NSAIDs or increased PGE2 due to exogenous delivery dictates MuSC function, which determines the outcome of regeneration. The markedly enhanced and accelerated repair of damaged muscles following intramuscular delivery of PGE2 suggests a previously unrecognized indication for this therapeutic agent.

Place, publisher, year, edition, pages
NATL ACAD SCIENCES, 2017
Keyword
muscle stem cells, PGE2, regeneration, NSAIDs, strength
National Category
Medical Biotechnology
Identifiers
urn:nbn:se:kth:diva-211007 (URN)10.1073/pnas.1705420114 (DOI)000404108400040 ()2-s2.0-85021450503 (Scopus ID)
Note

QC 20170713

Available from: 2017-07-13 Created: 2017-07-13 Last updated: 2017-07-13Bibliographically approved
Sadanandan, S. K., Baltekin, O., Magnusson, K. E. G., Boucharin, A., Ranefall, P., Jalden, J., . . . Wahlby, C. (2016). Segmentation and Track-Analysis in Time-Lapse Imaging of Bacteria. IEEE Journal on Selected Topics in Signal Processing, 10(1), 174-184.
Open this publication in new window or tab >>Segmentation and Track-Analysis in Time-Lapse Imaging of Bacteria
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2016 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 1, p. 174-184Article in journal (Refereed) Published
Abstract [en]

In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

Place, publisher, year, edition, pages
IEEE Communications Society, 2016
Keyword
E. coli, microscopy, segmentation, time-lapse, tracking
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-183199 (URN)10.1109/JSTSP.2015.2491304 (DOI)000369495900015 ()2-s2.0-84962911002 (Scopus ID)
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 616047
Note

QC 20160303

Available from: 2016-03-03 Created: 2016-03-03 Last updated: 2017-11-30Bibliographically approved
Magnusson, K. E. G., Jaldén, J., Gilbert, P. M. & Blau, H. M. (2015). Global linking of cell tracks using the Viterbi algorithm. IEEE Transactions on Medical Imaging, 34(4), 911-929.
Open this publication in new window or tab >>Global linking of cell tracks using the Viterbi algorithm
2015 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 34, no 4, p. 911-929Article in journal (Refereed) Published
Abstract [en]

Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm.

Place, publisher, year, edition, pages
IEEE Press, 2015
Keyword
Cell Tracking, Multiple Target Tracking, Data Association, Track Linking, Viterbi Algorithm, Dynamic Programming
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-159230 (URN)10.1109/TMI.2014.2370951 (DOI)000352533200008 ()2-s2.0-84926475283 (Scopus ID)
Funder
Swedish Research Council, 621-2011-5884
Note

QC 20150518

Available from: 2015-01-26 Created: 2015-01-26 Last updated: 2017-12-05Bibliographically approved
Magnusson, K. & Jaldén, J. (2015). Tracking of non-brownian particles using the Viterbi algorithm. In: Proceedings - International Symposium on Biomedical Imaging: . Paper presented at 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, 16 April 2015 through 19 April 2015 (pp. 380-384). IEEE conference proceedings.
Open this publication in new window or tab >>Tracking of non-brownian particles using the Viterbi algorithm
2015 (English)In: Proceedings - International Symposium on Biomedical Imaging, IEEE conference proceedings, 2015, p. 380-384Conference paper, Published paper (Refereed)
Abstract [en]

We present a global tracking algorithm for tracking particles with dynamic motion models. The tracking algorithm augments a existing global track linking algorithm based on the Viterbi algorithm with a Gaussian Mixture Probability Hypothesis Density filter. This allows the tracking algorithm to use the target velocities to link tracks. The algorithm can handle clutter, missed detections, and random appearance and disappearance of particles in the field of view. The algorithm can also handle targets that switch between different motion models according to a Markov process. The algorithm is evaluated on the synthetic datasets used in the ISBI 2012 Particle Tracking Challenge, which simulate vesicles, receptors, microtubules, and viruses at different particle densities and signal to noise ratios. The evaluation shows that our algorithm performs well across a wide range of particle tracking problems in both 2D and 3D.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keyword
GM-PHD, Particle tracking, Viterbi algorithm, Markov processes, Medical imaging, Motion analysis, Probability density function, Signal to noise ratio, Target tracking, Tracking (position), Viruses, Dynamic motion models, Gaussian mixture probability hypothesis density filters, Linking algorithms, Non-brownian particles, Synthetic datasets, Tracking algorithm, Algorithms
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-181653 (URN)10.1109/ISBI.2015.7163892 (DOI)000380546000091 ()2-s2.0-84944319065 (Scopus ID)9781479923748 (ISBN)
External cooperation:
Conference
12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, 16 April 2015 through 19 April 2015
Note

QC 20160316

Available from: 2016-03-16 Created: 2016-02-02 Last updated: 2016-09-05Bibliographically approved
Maska, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., Ederra, C., . . . Ortiz-de-Solorzano, C. (2014). A benchmark for comparison of cell tracking algorithms. Bioinformatics, 30(11), 1609-1617.
Open this publication in new window or tab >>A benchmark for comparison of cell tracking algorithms
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2014 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 11, p. 1609-1617Article in journal (Refereed) Published
Abstract [en]

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.

National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-147726 (URN)10.1093/bioinformatics/btu080 (DOI)000337040200016 ()2-s2.0-84901355271 (Scopus ID)
Funder
Swedish Research Council, 621-2011-5884
Note

QC 20140707

Available from: 2014-07-07 Created: 2014-07-03 Last updated: 2017-12-05Bibliographically approved
Chenouard, N., Smal, I., de Chaumont, F., Maska, M., Sbalzarini, I. F., Gong, Y., . . . Meijering, E. (2014). Objective comparison of particle tracking methods. Nature Methods, 11(3), 281-U247.
Open this publication in new window or tab >>Objective comparison of particle tracking methods
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2014 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 11, no 3, p. 281-U247Article in journal (Refereed) Published
Abstract [en]

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Keyword
Atomic-Force Microscopy, Automated Tracking, Fluorescence Microscopy, Dynamic Properties, Video Microscopy, Living Cells, Images, Segmentation, Algorithms, Transport
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-144559 (URN)10.1038/nmeth.2808 (DOI)000332086100020 ()2-s2.0-84895512107 (Scopus ID)
Funder
Swedish Research Council, 621-2011-5884EU, FP7, Seventh Framework Programme
Note

QC 20140425

Available from: 2014-04-25 Created: 2014-04-24 Last updated: 2017-12-05Bibliographically approved
Magnusson, K. E. G. & Jaldén, J. (2012). A batch algorithm using iterative application of the Viterbi algorithm to track cells and construct cell lineages. In: Proceedings - International Symposium on Biomedical Imaging: . Paper presented at 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012; Barcelona; 2 May 2012 through 5 May 2012 (pp. 382-385). Institute of Electrical and Electronics Engineers.
Open this publication in new window or tab >>A batch algorithm using iterative application of the Viterbi algorithm to track cells and construct cell lineages
2012 (English)In: Proceedings - International Symposium on Biomedical Imaging, Institute of Electrical and Electronics Engineers , 2012, p. 382-385Conference paper, Published paper (Refereed)
Abstract [en]

Advances in microscope hardware in the last couple of decades have made it possible to acquire large data sets with image sequences of living cells grown in cell culture. This has led to a demand for automated ways of analyzing the acquired images. This article presents a new algorithm for tracking cells and constructing cell lineages in such image sequences. The algorithm uses information from the entire sequence to make local decisions about cell tracks and can therefore make more robust decisions than algorithms that process the data sequentially. It also incorporates image-based likelihoods of cell division and cell death into the tracking, without having to resort to separate detection algorithms or post processing of tracks. The algorithm consists of a scoring function to rank tracks and an iterative algorithm that searches for the highest scoring tracks, in a computationally efficient way, using the Viterbi algorithm.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers, 2012
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
Keyword
Cell Tracking, Data Association, Dynamic Programming, Multiple Target Tracking, Viterbi Algorithm
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-101503 (URN)10.1109/ISBI.2012.6235564 (DOI)000312384100096 ()2-s2.0-84864832263 (Scopus ID)978-145771858-8 (ISBN)
Conference
9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012; Barcelona; 2 May 2012 through 5 May 2012
Funder
ICT - The Next Generation
Note

QC 20120905

Available from: 2012-09-05 Created: 2012-08-30 Last updated: 2013-10-02Bibliographically approved
Gilbert, P. M., Corbel, S., Doyonnas, R., Havenstrite, K., Magnusson, K. E. G. & Blau, H. M. (2012). A single cell bioengineering approach to elucidate mechanisms of adult stem cell self-renewal. Integrative Biology, 4(4), 360-367.
Open this publication in new window or tab >>A single cell bioengineering approach to elucidate mechanisms of adult stem cell self-renewal
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2012 (English)In: Integrative Biology, ISSN 1757-9694, E-ISSN 1757-9708, Vol. 4, no 4, p. 360-367Article in journal (Refereed) Published
Abstract [en]

The goal of regenerative medicine is to restore form and function to damaged and aging tissues. Adult stem cells, present in tissues such as skeletal muscle, comprise a reservoir of cells with a remarkable capacity to proliferate and repair tissue damage. Muscle stem cells, known as satellite cells, reside in a quiescent state in an anatomically distinct compartment, or niche, ensheathed between the membrane of the myofiber and the basal lamina. Recently, procedures for isolating satellite cells were developed and experiments testing their function upon transplantation into muscles revealed an extraordinary potential to contribute to muscle fibers and access and replenish the satellite cell compartment. However, these properties are rapidly lost once satellite cells are plated in culture. Accordingly, elucidating the role of extrinsic factors in controlling muscle stem cell fate, in particular self-renewal, is critical. Through careful design of bioengineered culture platforms, analysis of specific proteins presented to stem cells is possible. Critical to the success of the approach is single cell analysis, as more rapidly proliferating progenitors may mask the behavior of stem cells that proliferate slowly. Bioengineering approaches provide a potent means of gaining insight into the role of extrinsic factors in the stem cell microenvironment on stem cell function and the mechanisms that control their diverse fates. Ultimately, the multidisciplinary approach presented here will lead to novel therapeutic strategies for degenerative diseases.

National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-93661 (URN)10.1039/c2ib00148a (DOI)000302017100002 ()2-s2.0-84859128568 (Scopus ID)
Note
QC 20120424Available from: 2012-04-24 Created: 2012-04-23 Last updated: 2017-12-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5329-575X

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