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Publications (10 of 22) Show all publications
Siadat, M., Aghazadeh, N., Akbarifard, F., Brismar, H. & Öktem, O. (2019). Joint Image Deconvolution and Separation Using Mixed Dictionaries. IEEE Transactions on Image Processing, 28(8), 3936-3945
Open this publication in new window or tab >>Joint Image Deconvolution and Separation Using Mixed Dictionaries
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2019 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 28, no 8, p. 3936-3945Article in journal (Refereed) Published
Abstract [en]

The task of separating an image into distinct components that represent different features plays an important role in many applications. Traditionally, such separation techniques are applied once the image in question has been reconstructed from measured data. We propose an efficient iterative algorithm, where reconstruction is performed jointly with the task of separation. A key assumption is that the image components have different sparse representations. The algorithm is based on a scheme that minimizes a functional composed of a data discrepancy term and the l(1)-norm of the coefficients of the different components with respect to their corresponding dictionaries. The performance is demonstrated for joint 2D deconvolution and separation into curve- and point-like components, and tests are performed on synthetic data as well as experimental stimulated emission depletion and confocal microscopy data. Experiments show that such a joint approach outperforms a sequential approach, where one first deconvolves data and then applies image separation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Inverse problems, image separation, sparse recovery, curvelets, wavelets
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-255299 (URN)10.1109/TIP.2019.2903316 (DOI)000472609200006 ()30843839 (PubMedID)2-s2.0-85067800119 (Scopus ID)
Note

QC 20190730

Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-07-30Bibliographically approved
Bergstrand, J., Xu, L., Miao, X., Li, N., Öktem, O., Franzen, B., . . . Widengren, J. (2019). Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells. Nanoscale, 11(20), 10023-10033
Open this publication in new window or tab >>Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells
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2019 (English)In: Nanoscale, ISSN 2040-3364, E-ISSN 2040-3372, Vol. 11, no 20, p. 10023-10033Article in journal (Refereed) Published
Abstract [en]

Protein contents in platelets are frequently changed upon tumor development and metastasis. However, how cancer cells can influence protein-selective redistribution and release within platelets, thereby promoting tumor development, remains largely elusive. With fluorescence-based super-resolution stimulated emission depletion (STED) imaging we reveal how specific proteins, implicated in tumor progression and metastasis, re-distribute within platelets, when subject to soluble activators (thrombin, adenosine diphosphate and thromboxane A2), and when incubated with cancer (MCF-7, MDA-MB-231, EFO21) or non-cancer cells (184A1, MCF10A). Upon cancer cell incubation, the cell-adhesion protein P-selectin was found to re-distribute into circular nano-structures, consistent with accumulation into the membrane of protein-storing alpha-granules within the platelets. These changes were to a significantly lesser extent, if at all, found in platelets incubated with normal cells, or in platelets subject to soluble platelet activators. From these patterns, we developed a classification procedure, whereby platelets exposed to cancer cells, to non-cancer cells, soluble activators, as well as non-activated platelets all could be identified in an automatic, objective manner. We demonstrate that STED imaging, in contrast to electron and confocal microscopy, has the necessary spatial resolution and labelling efficiency to identify protein distribution patterns in platelets and can resolve how they specifically change upon different activations. Combined with image analyses of specific protein distribution patterns within the platelets, STED imaging can thus have a role in future platelet-based cancer diagnostics and therapeutic monitoring. The presented approach can also bring further clarity into fundamental mechanisms for cancer cell-platelet interactions, and into non-contact cell-to-cell interactions in general.

Place, publisher, year, edition, pages
ROYAL SOC CHEMISTRY, 2019
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-254018 (URN)10.1039/c9nr01967g (DOI)000469246100020 ()31086875 (PubMedID)
Note

Qc 20190814

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-08-14Bibliographically approved
Lunz, S., Öktem, O. & Schönlieb, C.-B. -. (2018). Adversarial regularizers in inverse problems. In: Advances in Neural Information Processing Systems: . Paper presented at 32nd Conference on Neural Information Processing Systems, NeurIPS 2018, 2 December 2018 through 8 December 2018 (pp. 8507-8516). Neural information processing systems foundation
Open this publication in new window or tab >>Adversarial regularizers in inverse problems
2018 (English)In: Advances in Neural Information Processing Systems, Neural information processing systems foundation , 2018, p. 8507-8516Conference paper, Published paper (Refereed)
Abstract [en]

Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regularization functional. The network learns to discriminate between the distribution of ground truth images and the distribution of unregularized reconstructions. Once trained, the network is applied to the inverse problem by solving the corresponding variational problem. Unlike other data-based approaches for inverse problems, the algorithm can be applied even if only unsupervised training data is available. Experiments demonstrate the potential of the framework for denoising on the BSDS dataset and for computed tomography reconstruction on the LIDC dataset.

Place, publisher, year, edition, pages
Neural information processing systems foundation, 2018
Keywords
Computerized tomography, Differential equations, Image reconstruction, Medical imaging, Medical problems, Problem solving, Data-driven approach, De-noising, Ground truth, Model-based method, Tomography reconstruction, Unsupervised training, Variational problems, Variational regularization, Inverse problems
National Category
Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-252271 (URN)000461852003010 ()2-s2.0-85064820716 (Scopus ID)
Conference
32nd Conference on Neural Information Processing Systems, NeurIPS 2018, 2 December 2018 through 8 December 2018
Note

QC20190610

Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2019-06-10Bibliographically approved
Chen, C. & Öktem, O. (2018). Indirect image registration with large diffeomorphic deformations. SIAM Journal on Imaging Sciences, 11(1), 575-617
Open this publication in new window or tab >>Indirect image registration with large diffeomorphic deformations
2018 (English)In: SIAM Journal on Imaging Sciences, ISSN 1936-4954, E-ISSN 1936-4954, Vol. 11, no 1, p. 575-617Article in journal (Refereed) Published
Abstract [en]

This paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting, where a template is registered against a target that is given through indirect noisy observations. The registration uses diffeomorphisms that transform the template through a (group) action. These diffeomorphisms are generated by solving a flow equation that is defined by a velocity field with certain regularity. The theoretical analysis includes a proof that indirect image registration has solutions (existence) that are stable and that converge as the data error tends to zero, so it becomes a well-defined regularization method. The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data. 

Place, publisher, year, edition, pages
Society for Industrial and Applied Mathematics Publications, 2018
Keywords
Diffeomorphisms, Image reconstruction, Indirect image registration, Large deformations, Shape regularization, Shape theory, Tomography, Deformation, Image registration, Velocity, Diffeomorphic deformation, Large deformation diffeomorphic metric mappings, Noisy observations, Regularization methods, Velocity field
National Category
Mathematics
Identifiers
urn:nbn:se:kth:diva-227421 (URN)10.1137/17M1134627 (DOI)000428946200019 ()2-s2.0-85045630390 (Scopus ID)
Note

Export Date: 9 May 2018; Article; Funding details: KTH, Kungliga Tekniska Högskolan; Funding details: 100190, CAS, Chinese Academy of Sciences; Funding details: AM13-0049, SSF, Stiftelsen för Strategisk Forskning; Funding details: 11301520, NSFC, National Natural Science Foundation of China; Funding details: NSFC, National Natural Science Foundation of China; Funding text: ∗Received by the editors June 14, 2017; accepted for publication (in revised form) December 18, 2017; published electronically March 1, 2018. http://www.siam.org/journals/siims/11-1/M113462.html Funding: This work was supported by the Swedish Foundation for Strategic Research grant AM13-0049. The first author was also supported in part by the National Natural Science Foundation of China (NSFC) under grant 11301520. †LSEC, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China (chench@lsec.cc.ac.cn). ‡Department of Mathematics, KTH–Royal Institute of Technology, 100 44 Stockholm, Sweden (ozan@kth.se). QC 20180529

Available from: 2018-05-29 Created: 2018-05-29 Last updated: 2018-05-29Bibliographically approved
Adler, J. & Öktem, O. (2018). Learned Primal-Dual Reconstruction. IEEE Transactions on Medical Imaging, 37(6), 1322-1332
Open this publication in new window or tab >>Learned Primal-Dual Reconstruction
2018 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 6, p. 1322-1332Article in journal (Refereed) Published
Abstract [en]

We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it does not depend on any initial reconstruction such as filtered back-projection (FBP). We compare performance of the proposed method on low dose computed tomography reconstruction against FBP, total variation (TV), and deep learning based post-processing of FBP. For the Shepp-Logan phantom we obtain >6 dB peak signal to noise ratio improvement against all compared methods. For human phantoms the corresponding improvement is 6.6 dB over TV and 2.2 dB over learned post-processing along with a substantial improvement in the structural similarity index. Finally, our algorithm involves only ten forward-back-projection computations, making the method feasible for time critical clinical applications.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
Keywords
Inverse problems, tomography, deep learning, primal-dual, optimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-231206 (URN)10.1109/TMI.2018.2799231 (DOI)000434302700004 ()29870362 (PubMedID)2-s2.0-85041342868 (Scopus ID)
Note

QC 20180629

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2019-10-18Bibliographically approved
Siadat, M., Aghazadeh, N. & Öktem, O. (2018). Reordering for improving global Arnoldi-Tikhonov method in image restoration problems. Signal, Image and Video Processing, 12(3), 497-504
Open this publication in new window or tab >>Reordering for improving global Arnoldi-Tikhonov method in image restoration problems
2018 (English)In: Signal, Image and Video Processing, ISSN 1863-1703, E-ISSN 1863-1711, Vol. 12, no 3, p. 497-504Article in journal (Refereed) Published
Abstract [en]

This paper discusses the solution of large-scale linear discrete ill-posed problems arising from image restoration problems. Since the scale of the problem is usually very large, the computations with the blurring matrix can be very expensive. In this regard, we consider problems in which the coefficient matrix is the sum of Kronecker products of matrices to benefit the computation. Here, we present an alternative approach based on reordering of the image approximations obtained with the global Arnoldi-Tikhonov method. The ordering of the intensities is such that it makes the image approximation monotonic and thus minimizes the finite differences norm. We present theoretical properties of the method and numerical experiments on image restoration.

Place, publisher, year, edition, pages
Springer London, 2018
Keywords
Inverse problems, Regularization methods, Reordering, Kronecker product
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-240178 (URN)10.1007/s11760-017-1185-5 (DOI)000426815900013 ()2-s2.0-85030860938 (Scopus ID)
Note

QC 20181218

Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2019-10-10Bibliographically approved
Tavabi, A. H., Beleggia, M., Migunov, V., Savenko, A., Öktem, O., Dunin-Borkowski, R. E. & Pozzi, G. (2018). Tunable Ampere phase plate for low dose imaging of biomolecular complexes. Scientific Reports, 8, Article ID 5592.
Open this publication in new window or tab >>Tunable Ampere phase plate for low dose imaging of biomolecular complexes
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2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 5592Article in journal (Refereed) Published
Abstract [en]

A novel device that can be used as a tunable support-free phase plate for transmission electron microscopy of weakly scattering specimens is described. The device relies on the generation of a controlled phase shift by the magnetic field of a segment of current-carrying wire that is oriented parallel or antiparallel to the electron beam. The validity of the concept is established using both experimental electron holographic measurements and a theoretical model based on Ampere's law. Computer simulations are used to illustrate the resulting contrast enhancement for studies of biological cells and macromolecules.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2018
National Category
Condensed Matter Physics
Identifiers
urn:nbn:se:kth:diva-228130 (URN)10.1038/s41598-018-23100-3 (DOI)000429095600001 ()29618785 (PubMedID)2-s2.0-85044965282 (Scopus ID)
Note

QC 20180518

Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2018-05-18Bibliographically approved
Adler, J. & Öktem, O. (2017). Solving ill-posed inverse problems using iterative deep neural networks. Inverse Problems, 33(12), Article ID 124007.
Open this publication in new window or tab >>Solving ill-posed inverse problems using iterative deep neural networks
2017 (English)In: Inverse Problems, ISSN 0266-5611, E-ISSN 1361-6420, Vol. 33, no 12, article id 124007Article in journal (Refereed) Published
Abstract [en]

We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the 'gradient' component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 x 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2017
Keywords
tomography, deep learning, gradient descent, regularization
National Category
Mathematics
Identifiers
urn:nbn:se:kth:diva-219496 (URN)10.1088/1361-6420/aa9581 (DOI)000416015300001 ()2-s2.0-85038424472 (Scopus ID)
Note

QC 20171207

Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2019-10-18Bibliographically approved
Dong, G., Patrone, A. R., Scherzer, O. & Öktem, O. (2015). Infinite dimensional optimization models and PDEs for dejittering. In: 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015: . Paper presented at 31 May 2015 through 4 June 2015 (pp. 678-689). Elsevier, 9087
Open this publication in new window or tab >>Infinite dimensional optimization models and PDEs for dejittering
2015 (English)In: 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015, Elsevier, 2015, Vol. 9087, p. 678-689Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we do a systematic investigation of continuous methods for pixel, line pixel and line dejittering. The basis for these investigations are the discrete line dejittering algorithm of Nikolova and the partial differential equation of Lenzen et al for pixel dejittering. To put these two different worlds in perspective we find infinite dimensional optimization algorithms linking to the finite dimensional optimization problems and formal flows associated with the infinite dimensional optimization problems. Two different kinds of optimization problems will be considered: Dejittering algorithms for determining the displacement and displacement error correction formulations, which correct the jittered image, without estimating the jitter. As a by-product we find novel variational methods for displacement error regularization and unify them into one family. The second novelty is a comprehensive comparison of the different models for different types of jitter, in terms of efficiency of reconstruction and numerical complexity.

Place, publisher, year, edition, pages
Elsevier, 2015
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743
Keywords
Dejittering, Nonlinear evolutionPDEs, Variationalmethods, Algorithms, Computer vision, Differential equations, Error correction, Jitter, Pixels, Comprehensive comparisons, Infinite dimensional, Numerical complexity, Optimization algorithms, Optimization problems, Optimization
National Category
Mathematics
Identifiers
urn:nbn:se:kth:diva-177256 (URN)10.1007/978-3-319-18461-6_54 (DOI)2-s2.0-84931081955 (Scopus ID)9783319184609 (ISBN)
Conference
31 May 2015 through 4 June 2015
Note

QC 20151119

Available from: 2015-11-19 Created: 2015-11-17 Last updated: 2015-11-19Bibliographically approved
Öktem, O. (2015). Mathematics of electron tomography. In: Handbook of Mathematical Methods in Imaging: Volume 1, Second Edition: (pp. 937-1031). Springer
Open this publication in new window or tab >>Mathematics of electron tomography
2015 (English)In: Handbook of Mathematical Methods in Imaging: Volume 1, Second Edition, Springer, 2015, p. 937-1031Chapter in book (Other academic)
Abstract [en]

This survey starts with a brief description of the scientific relevance of electron tomography in life sciences followed by a survey of image formation models. In the latter, the scattering of electrons against a specimen is modeled by the Schrödinger equation, and the image formation model is completed by adding a description of the transmission electron microscope optics and detector. Electron tomography can then be phrased as an inverse scattering problem and attention is now turned to describing mathematical approaches for solving that reconstruction problem. This part starts out by explaining challenges associated with the aforementioned inverse problem, such as the extremely low signalto- noise ratio in the data and the severe ill-posedness due to incomplete data, which naturally brings up the issue of choosing a regularization method for reconstruction. Here, the review surveys both methods that have been developed, as well as pointing to new promising approaches. Some of the regularization methods are also tested on simulated and experimental data. As a final note, this is not a traditional mathematical review in the sense that focus here is on the application to electron tomography rather than on describing mathematical techniques that underly proofs of key theorems.

Place, publisher, year, edition, pages
Springer, 2015
National Category
Mathematics
Identifiers
urn:nbn:se:kth:diva-181248 (URN)10.1007/978-1-4939-0790-8_43 (DOI)2-s2.0-84944626100 (Scopus ID)9781493907908 (ISBN)
Note

QC 20160218

Available from: 2016-02-18 Created: 2016-01-29 Last updated: 2016-02-18Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1118-6483

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