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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…