Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
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. Given the micrometer...
Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
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 state fMRI data,...
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., peritumoral edema,...
What is the best data augmentation for 3D brain tumor segmentation?
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 fully explored...
Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE
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 and an...
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?
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. Context-aware...
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification
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 pathways. The...
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
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) is a deep...
Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN
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, which makes it...
Refacing: reconstructing anonymized facial features using GANs
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 deep learning may...
Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN
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 CycleGAN can be...
Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity),...
Graph Spectral Characterization of Brain Cortical Morphology
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...
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion...