Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE

2020/09/13 by Mehdi Foroozandeh, Foroozandeh, Mehdi, Anders Eklúnd +2
Computer Science · #AI in cancer detection #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Medical Image Segmentation Techniques

paper · pdf · doi:10.48550/arxiv.2009.05946

openalex publication_date 2020/09/13 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

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.

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