论文标题
通过结合渐进式生长gan和spade合成脑肿瘤图像和注释
Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE
论文作者
论文摘要
培训细分网络需要大量注释的数据集，但是手动注释耗时且昂贵。在这里，我们研究了噪声到图像gan和图像到图像的组合是否可用于合成逼真的脑肿瘤图像以及相应的肿瘤注释（标签），以实质上增加训练图像的数量。噪声到图像gan用于合成新的标签图像，而图像到图像gan从标签图像中生成相应的MR图像。我们的结果表明，这两个gan可以合成看起来很现实的标签图像和MR图像，并且添加合成图像可以改善分割性能，尽管效果很小。
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.
