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Found 20 papers, 9 papers with code
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.
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
Here, we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image translation of T1- and T2-weighted MR images, by comparing generated synthetic MR images to ground truth images.
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.
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.
Characterization of Brain Cortical Morphology Using Localized Topology-Encoding Graphs
Spectral features of these graphs are then studied and proposed as descriptors of cortical morphology.
Graph Spectral Characterization of Brain Cortical Morphology
The human brain cortical layer has a convoluted morphology that is unique to each individual.
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
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.
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.
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, necrotic core, enhancing and non-enhancing tumour core.
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.
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?
The BraTS2020 dataset was used to train and test two standard 3D U-Net models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP).
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.
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.
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.
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification
However, it is hard to estimate the impact of noise on downstream tasks based only on such qualitative assessments.
Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?
Large annotated datasets are required to train segmentation networks.
Efficient brain age prediction from 3D MRI volumes using 2D projections
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.
Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images
Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high-quality synthetic images.
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80% - 90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small.
