Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE
arXiv:2009.05946
Abstract
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 image-to-image GAN can be used to synthesize realistic brain tumor images as well as the corresponding tumor annotations (labels), to substantially increase the number of training images. The noise-to-image GAN is used to synthesize new label images, while the image-to-image GAN generates the corresponding MR image from the label image. Our results indicate that the two GANs can synthesize label images and MR images that look realistic, and that adding synthetic images improves the segmentation performance, although the effect is small.
References in corpus (8)
- Adam: A Method for Stochastic Optimization
- A Survey on Deep Learning in Medical Image Analysis
- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
- GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
- Synthetic Medical Images from Dual Generative Adversarial Networks
- Boundary-Seeking Generative Adversarial Networks
- Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN
- Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN