3.7k citations · 4.1k across the 26 of their papers we have counts for
31 papers
Efficient brain age prediction from 3D MRI volumes using 2D projections
Johan Jönemo, Muhammad Usman Akbar, Robin Kämpe +2
Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we d…
Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?
Måns Larsson, Muhammad Usman Akbar, Anders Eklund
Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may al…
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification
Shreyas Fadnavis, Jens Sjölund, Anders Eklund +1
Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue micro-architecture, which can, in turn, be used to reconstruct tissue microstructure and white matter pa…
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini
In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Give…
Bayesian Modeling of Effective and Functional Brain Connectivity using Hierarchical Vector Autoregressions
Bertil Wegmann, Anders Lundquist, Anders Eklund +1
Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression (VAR) hierarchical model…
Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
Johan Jönemo, David Abramian, Anders Eklund
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting…
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?
Iulian Emil Tampu, Neda Haj-Hosseini, Anders Eklund
Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning from magnetic resonance (MR) images. C…
What is the best data augmentation for 3D brain tumor segmentation?
Marco Domenico Cirillo, David Abramian, Anders Eklund
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been f…
Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE
Mehdi Foroozandeh, Anders Eklund
Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly. We here investigate if the combination of a noise-to-image GAN…
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
Marco Domenico Cirillo, David Abramian, Anders Eklund
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., perit…
Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN
Anders Eklund
Deep learning requires large datasets for training (convolutional) networks with millions of parameters. In neuroimaging, there are few open datasets with more than 100 subjects, w…
Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering
David Abramian, Martin Larsson, Anders Eklund +1
Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD con…
Anatomically informed Bayesian spatial priors for fMRI analysis
David Abramian, Per Sidén, Hans Knutsson +2
Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical bounda…
Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN
David Abramian, Anders Eklund
Registration between an fMRI volume and a T1-weighted volume is challenging, since fMRI volumes contain geometric distortions. Here we present preliminary results showing that 3D C…
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Per Sidén, Finn Lindgren, David Bolin +2
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors has been shown to produce state-of-the-art activity maps…
Graph Spectral Characterization of Brain Cortical Morphology
Sevil Maghsadhagh, Anders Eklund, Hamid Behjat
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 st…
Characterization of Brain Cortical Morphology Using Localized Topology-Encoding Graphs
Sevil Maghsadhagh, Mousa Shamsi, Anders Eklund +1
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 st…
Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests
Anders Eklund, Hans Knutsson, Thomas E. Nichols
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…
Refacing: reconstructing anonymized facial features using GANs
David Abramian, Anders Eklund
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in d…
Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
Xuan Gu, Hans Knutsson, Markus Nilsson +1
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusi…
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
Per Welander, Simon Karlsson, Anders Eklund
In medical imaging, a general problem is that it is costly and time consuming to collect high quality data from healthy and diseased subjects. Generative adversarial networks (GANs…
Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
Anders Eklund, Hans Knutsson, Thomas E Nichols
Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intens…
Bayesian uncertainty quantification in linear models for diffusion MRI
Jens Sjölund, Anders Eklund, Evren Özarslan +3
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. Se…
Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis
Josef Wilzén, Anders Eklund, Mattias Villani
Background: Inference from fMRI data faces the challenge that the hemodynamic system that relates neural activity to the observed BOLD fMRI signal is unknown. New Method: We propos…