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del Aguila, P. & Jaldén, J. (2018). Cell detection by functional inverse diffusion and non-negative group sparsity – Part I: Modeling and Inverse Problems. IEEE Transactions on Signal Processing, 66(20), 5407-5421
Open this publication in new window or tab >>Cell detection by functional inverse diffusion and non-negative group sparsity – Part I: Modeling and Inverse Problems
2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 20, p. 5407-5421Article in journal (Refereed) Published
Abstract [en]

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Inverse problems, Biomedical imaging, Convex optimization, Source localization, Biological modeling
National Category
Signal Processing Medical Image Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233824 (URN)10.1109/TSP.2018.2868258 (DOI)2-s2.0-85052785365 (Scopus ID)
Funder
Swedish Research Council, 2015-04026
Note

QC 20180918

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2018-09-18Bibliographically approved
del Aguila, P. & Jaldén, J. (2018). Cell detection by functional inverse diffusion and non-negative group sparsity – Part II: Proximal optimization and Performance evaluation. IEEE Transactions on Signal Processing, 66(20), 5422-5437
Open this publication in new window or tab >>Cell detection by functional inverse diffusion and non-negative group sparsity – Part II: Proximal optimization and Performance evaluation
2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 20, p. 5422-5437Article in journal (Refereed) Published
Abstract [en]

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this second part, we focus on our algorithmic contributions. We provide an algorithm for functional inverse diffusion that solves the variational problem we posed in Part I. As part of the derivation of this algorithm, we present the proximal operator for the non-negative group-sparsity regularizer, which is a novel result that is of interest in itself, also in comparison to previous results on the proximal operator of a sum of functions. We then present a discretized approximated implementation of our algorithm and evaluate it both in terms of operational cell-detection metrics and in terms of distributional optimal-transport metrics.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Proximal operator, Non-negative group sparsity, Functional optimization, Biomedical imaging, Source localization
National Category
Signal Processing Medical Image Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233827 (URN)10.1109/TSP.2018.2868256 (DOI)2-s2.0-85052808396 (Scopus ID)
Funder
Swedish Research Council, 2015-04026
Note

QC 20180918

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2018-09-18Bibliographically approved
del Aguila Pla, P. & Jaldén, J. (2018). Cell detection on image-based immunoassays. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018): . Paper presented at 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, April 4-7, 2018 (pp. 431-435). IEEE
Open this publication in new window or tab >>Cell detection on image-based immunoassays
2018 (English)In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, p. 431-435Conference paper, Published paper (Refereed)
Abstract [en]

Cell detection and counting in the image-based ELISPOT and Fluorospot immunoassays is considered a bottleneck.The task has remained hard to automatize, and biomedical researchers often have to rely on results that are not accurate.Previously proposed solutions are heuristic, and data-based solutions are subject to a lack of objective ground truth data. In this paper, we analyze a partial differential equations model for ELISPOT, Fluorospot, and assays of similar design. This leads us to a mathematical observation model forthe images generated by these assays. We use this model to motivate a methodology for cell detection. Finally, we provide a real-data example that suggests that this cell detection methodology and a human expert perform comparably.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Inverse problems, Optimization, Source localization, Immunoassays
National Category
Signal Processing Medical Image Processing
Research subject
Technology and Health; Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-223933 (URN)10.1109/ISBI.2018.8363609 (DOI)978-1-5386-3636-7 (ISBN)978-1-5386-3637-4 (ISBN)978-1-5386-3635-0 (ISBN)
Conference
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, April 4-7, 2018
Funder
Swedish Research Council, 2015-04026
Note

QC 20180611

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-06-11Bibliographically approved
del Aguila Pla, P. & Jaldén, J. (2018). Convolutional group-sparse coding and source localization. In: : . Paper presented at 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15-20 April 2018, Calgary, AB, Canada (pp. 2776-2780). IEEE
Open this publication in new window or tab >>Convolutional group-sparse coding and source localization
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Acoustics, Speech and Signal Processing, E-ISSN 2379-190X
Keywords
Sparse representation, Source localization, Non-negative group sparsity
National Category
Signal Processing
Research subject
Electrical Engineering; Computer Science
Identifiers
urn:nbn:se:kth:diva-224253 (URN)10.1109/ICASSP.2018.8462235 (DOI)978-1-5386-4659-5 (ISBN)978-1-5386-4658-8 (ISBN)978-1-5386-4657-1 (ISBN)
Conference
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15-20 April 2018, Calgary, AB, Canada
Funder
Swedish Research Council, 2015-04026
Note

QC 20180917

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-09-18Bibliographically approved
Yu, W. & Jaldén, J. (2018). Perspectives in Signal Processing for Communications and Networking. IEEE signal processing magazine (Print), 35(5), 188-+
Open this publication in new window or tab >>Perspectives in Signal Processing for Communications and Networking
2018 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 35, no 5, p. 188-+Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-235453 (URN)10.1109/MSP.2018.2841413 (DOI)000443991800023 ()
Note

QC 20180927

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2018-09-27Bibliographically approved
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
Show others...
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
Tarighati, A., Gross, J. & Jaldén, J. (2017). Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks. IEEE Transactions on Signal Processing, 65(18), 4862-4873
Open this publication in new window or tab >>Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks
2017 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 18, p. 4862-4873Article in journal (Refereed) Published
Abstract [en]

We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under which the messages are sent through parallel access channels towards the fusion center. To avoid limited lifetime issues, we assume each sensor is capable of harvesting all the energy it needs for the communication from the environment. Each sensor has an energy buffer (battery) to save its harvested energy for use in other time intervals. Our key contribution is to formulate the problem of decentralized detection in a sensor network with energy harvesting devices. Our analysis is based on a queuing-theoretic model for the battery and we propose a sensor decision design method by considering long term energy management at the sensors. We show how the performance of the system changes for different battery capacities. We then numerically show how our findings can be used in the design of sensor networks with energy harvesting sensors.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-208654 (URN)10.1109/TSP.2017.2716909 (DOI)000405705900013 ()2-s2.0-85023178804 (Scopus ID)
Note

QC 20170706

Available from: 2017-06-10 Created: 2017-06-10 Last updated: 2018-09-19Bibliographically approved
Carlsson, H., Skog, I. & Jaldén, J. (2017). On-The-Fly Geometric Calibration of Inertial Sensor Arrays. In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN): . Paper presented at 8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN.
Open this publication in new window or tab >>On-The-Fly Geometric Calibration of Inertial Sensor Arrays
2017 (English)In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2017Conference paper, Published paper (Refereed)
Abstract [en]

We present a maximum likelihood estimator for estimating the positions of accelerometers in an inertial sensor array. This method simultaneously estimates the positions of the accelerometers and the motion dynamics of the inertial sensor array and, therefore, does not require a predefined motion sequence nor any external equipment. Using an iterative block coordinate descent optimization strategy, the calibration problem can be solved with a complexity that is linear in the number of time samples. The proposed method is evaluated by Monte-Carlo simulations of an inertial sensor array built out of 32 inertial measurement units. The simulation results show that, if the array experiences sufficient dynamics, the position error is inversely proportional to the number of time samples used in the calibration sequence. Further, results show that for the considered array geometry and motion dynamics in the order of 2000 degrees/s and 2000 degrees/s(2), the positions of the accelerometers can be estimated with an accuracy in the order of 10(-6) m using only 1000 time samples. This enables fast on-the-fly calibration of the geometric errors in an inertial sensor array by simply twisting it by hand for a few seconds.

Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-220651 (URN)000417415600018 ()978-1-5090-6299-7 (ISBN)
Conference
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-02-20Bibliographically approved
Tarighati, A., Gross, J. & Jaldén, J. (2016). Decentralized Detection in Energy Harvesting Wireless Sensor Networks. In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO): . Paper presented at 24th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2016, Budapest, HUNGARY (pp. 567-571). IEEE conference proceedings
Open this publication in new window or tab >>Decentralized Detection in Energy Harvesting Wireless Sensor Networks
2016 (English)In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE conference proceedings, 2016, p. 567-571Conference paper, Published paper (Refereed)
Abstract [en]

We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors are arranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy-unconstrained case.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-201278 (URN)10.1109/EUSIPCO.2016.7760312 (DOI)000391891900111 ()2-s2.0-85005959803 (Scopus ID)978-0-9928-6265-7 (ISBN)
Conference
24th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2016, Budapest, HUNGARY
Note

QC 20170215

Available from: 2017-02-15 Created: 2017-02-15 Last updated: 2017-02-15Bibliographically approved
Tarighati, A., Gross, J. & Jalden, J. (2016). Distributed detection in energy harvesting wireless sensor networks. In: European Signal Process. Conf. (EUSIPCO), Aug. 2016: . Paper presented at European Signal Process. Conf. (EUSIPCO). August 29-Sept. 2, 2016. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Distributed detection in energy harvesting wireless sensor networks
2016 (English)In: European Signal Process. Conf. (EUSIPCO), Aug. 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper, Published paper (Refereed)
Abstract [en]

We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors arearranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy-unconstrained case.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-189862 (URN)
External cooperation:
Conference
European Signal Process. Conf. (EUSIPCO). August 29-Sept. 2, 2016
Note

QC 20160907

Available from: 2016-07-20 Created: 2016-07-20 Last updated: 2016-11-02Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6630-243X

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