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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…
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…
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…
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…