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Eklund, Anders
Publications (10 of 41) Show all publications
Pernet, C., Marinazzo, D., Stippich, C., Beisteiner, R., Douw, L. & Eklund, A. (2019). A new repository to share brain tumour data: European Network for Brain Imaging of Tumours. In: : . Paper presented at Organization for Human Brain Mapping.
Open this publication in new window or tab >>A new repository to share brain tumour data: European Network for Brain Imaging of Tumours
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2019 (English)Conference paper (Refereed)
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-159519 (URN)
Conference
Organization for Human Brain Mapping
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12
Pham, T., Wårdell, K., Eklund, A. & Salerud, G. (2019). Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots. IEEE/CAA Journal of Automatica Sinica, 6(6), 1306-1317
Open this publication in new window or tab >>Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots
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2019 (English)In: IEEE/CAA Journal of Automatica Sinica, ISSN 2329-9266, Vol. 6, no 6, p. 1306-1317Article in journal (Refereed) Published
Abstract [en]

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson's disease (PD). A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease. Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long. With an attempt to avoid discomfort to participants in performing long physical tasks for data recording, this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory (LSTM) neural networks. Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture, fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Keywords
Deep learning, early Parkinson’s disease (PD), fuzzy recurrence plots, long short-term memory (LSTM) neural networks, pattern classification, short time series
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-161818 (URN)10.1109/JAS.2019.1911774 (DOI)
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-22Bibliographically approved
Eklund, A., Knutsson, H. & Nichols, T. E. (2019). Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates. Human Brain Mapping, 40(7), 2017-2032
Open this publication in new window or tab >>Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates
2019 (English)In: Human Brain Mapping, ISSN 1065-9471, E-ISSN 1097-0193, Vol. 40, no 7, p. 2017-2032Article in journal (Refereed) Published
Abstract [en]

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event‐related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one‐sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two‐sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

Keywords
cluster inference, false positives, functional magnetic resonance imaging, ICA FIX, permutation, physiological noise
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-152019 (URN)10.1002/hbm.24350 (DOI)000463153200002 ()30318709 (PubMedID)
Note

Funding agencies: Wellcome Trust [100309/Z/12/Z]; NIH [R01 EB015611]; Knut och Alice Wallenbergs Stiftelse; Linkoping University; Center for Industrial Information Technology (CENIIT); Swedish research council [2013-5229, 2017-04889]

Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2019-05-14
De Biase, A., Burlutskiy, N., Pinchaud, N. & Eklund, A. (2019). Deep Learning Data Augmentation Approach to Improve Cancer Segmentation Performance across Different Scanners. In: : . Paper presented at Nordic Symposium on Digital Pathology.
Open this publication in new window or tab >>Deep Learning Data Augmentation Approach to Improve Cancer Segmentation Performance across Different Scanners
2019 (English)Conference paper, Oral presentation only (Refereed)
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-159518 (URN)
Conference
Nordic Symposium on Digital Pathology
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-13
Gu, X., Knutsson, H., Nilsson, M. & Eklund, A. (2019). Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks. In: Felsberg M., Forssén PE., Sintorn IM., Unger J. (Ed.), Image Analysis: Lecture Notes in Computer Science. Paper presented at Scandinavian Conference on Image Analysis, SCIA (pp. 489-498). Springer Publishing Company
Open this publication in new window or tab >>Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
2019 (English)In: Image Analysis: Lecture Notes in Computer Science / [ed] Felsberg M., Forssén PE., Sintorn IM., Unger J., Springer Publishing Company, 2019, p. 489-498Conference paper, Published paper (Refereed)
Abstract [en]

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.

Place, publisher, year, edition, pages
Springer Publishing Company, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Diffusion MRI, Generative Adversarial Networks, CycleGAN, Distortion correction
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-158662 (URN)10.1007/978-3-030-20205-7_40 (DOI)978-3-030-20204-0 (ISBN)978-3-030-20205-7 (ISBN)
Conference
Scandinavian Conference on Image Analysis, SCIA
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-11-19
Maghsadhagh, S., Eklund, A. & Behjat, H. (2019). Graph Spectral Characterization of Brain Cortical Morphology. In: : . Paper presented at 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23-27 July 2019.
Open this publication in new window or tab >>Graph Spectral Characterization of Brain Cortical Morphology
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral metrics derived from these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the metrics are studied in the context of hemispheric asymmetry as well as gender dependent discrimination of cortical morphology.

Keywords
Morphology, Eigenvalues and eigenfunctions, Measurement, Shape, Laplace equations, Symmetric matrices, Encoding
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-160933 (URN)10.1109/EMBC.2019.8856468 (DOI)
Conference
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23-27 July 2019
Available from: 2019-10-15 Created: 2019-10-15 Last updated: 2019-10-25Bibliographically approved
Eklund, A. & Knutsson, H. (2019). Reply to Chen et al.: Parametric methods for cluster inference perform worse for two‐sided t‐tests. Human Brain Mapping, 40(5), 1689-1691
Open this publication in new window or tab >>Reply to Chen et al.: Parametric methods for cluster inference perform worse for two‐sided t‐tests
2019 (English)In: Human Brain Mapping, ISSN 1065-9471, E-ISSN 1097-0193, Vol. 40, no 5, p. 1689-1691Article in journal (Other (popular science, discussion, etc.)) Published
Abstract [en]

One‐sided t‐tests are commonly used in the neuroimaging field, but two‐sided tests should be the default unless a researcher has a strong reason for using a one‐sided test. Here we extend our previous work on cluster false positive rates, which used one‐sided tests, to two‐sided tests. Briefly, we found that parametric methods perform worse for two‐sided t‐tests, and that nonparametric methods perform equally well for one‐sided and two‐sided tests.

Keywords
cluster inference, false positives, fMRI, one‐sided, permutation, two‐sided
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-153286 (URN)10.1002/hbm.24465 (DOI)000460680400025 ()
Note

Funding agencies: NIH [R01 EB015611]; Wellcome Trust [100309/Z/12/Z]; Knut och Alice Wallenbergs Stiftelse; Linkoping University; Swedish Research Council [2017-04889, 2013-5229]; "la Caixa" Foundation; Vetenskapsradet

Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2019-04-01
Gu, X., Eklund, A., Özarslan, E. & Knutsson, H. (2019). Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI. Frontiers in Neuroinformatics, 13, Article ID 43.
Open this publication in new window or tab >>Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
2019 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed) Published
Abstract [en]

Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019
Keywords
Bootstrap, diffusion MRI, MAP-MRI, uncertainty, RtoP, NG, PA
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-157089 (URN)10.3389/fninf.2019.00043 (DOI)000471589200001 ()31244637 (PubMedID)
Note

Funding agencies:  Swedish Research Council [2015-05356]; Linkoping University Center for Industrial Information Technology (CENIIT); Knut and Alice Wallenberg Foundation project Seeing Organ Function; National Institute of Dental and Craniofacial Research (NIDCR); National

Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-11-19
Sjölund, J., Eklund, A., Özarslan, E., Herberthson, M., Bånkestad, M. & Knutsson, H. (2018). Bayesian uncertainty quantification in linear models for diffusion MRI. NeuroImage, 175, 272-285
Open this publication in new window or tab >>Bayesian uncertainty quantification in linear models for diffusion MRI
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2018 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 175, p. 272-285Article in journal (Refereed) Published
Abstract [en]

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.

Place, publisher, year, edition, pages
Academic Press, 2018
Keywords
Diffusion MRI, Uncertainty quantification, Signal estimation
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-147245 (URN)10.1016/j.neuroimage.2018.03.059 (DOI)000432949000023 ()29604453 (PubMedID)
Note

Funding agencies: Swedish Foundation for Strategic Research [AM13-0090]; Swedish Research Council CADICS Linneaus research environment; Swedish Research Council [2012-4281, 2013-5229, 2015-05356, 2016-04482]; Linkoping University Center for Industrial Information Technolog

Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-06-28
Gu, X., Sidén, P., Wegmann, B., Eklund, A., Villani, M. & Knutsson, H. (2017). Bayesian Diffusion Tensor Estimation with Spatial Priors. In: CAIP 2017: Computer Analysis of Images and Patterns. Paper presented at International Conference on Computer Analysis of Images and Patterns (pp. 372-383). , 10424
Open this publication in new window or tab >>Bayesian Diffusion Tensor Estimation with Spatial Priors
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2017 (English)In: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, p. 372-383Conference paper, Published paper (Refereed)
Abstract [en]

Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Keywords
Spatial regularization, Diffusion tensor, Spatial priors Markov chain, Monte Carlo Fractional anisotropy
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-139844 (URN)10.1007/978-3-319-64689-3_30 (DOI)000432085900030 ()978-3-319-64689-3 (ISBN)978-3-319-64688-6 (ISBN)
Conference
International Conference on Computer Analysis of Images and Patterns
Note

Funding agencies: Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy); Swedish Research Council [2015-05356, 2013-5229]; National Institute of Dental and Craniof

Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2019-11-19
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