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.
Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high-quality synthetic images.
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.
However, it is hard to estimate the impact of noise on downstream tasks based only on such qualitative assessments.
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.
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years.
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.
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).
Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly.
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.
Deep learning requires large datasets for training (convolutional) networks with millions of parameters.
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
Spectral features of these graphs are then studied and proposed as descriptors of cortical morphology.
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing.
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique.
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.
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space.