A multi-stage GAN for multi-organ chest X-ray image generation and segmentation
This addresses the lack of labeled data for medical image segmentation, which is a bottleneck for computer-aided diagnosis systems, but it is incremental as it builds on existing GAN methods.
The paper tackles the problem of multi-organ chest X-ray segmentation by proposing a multi-stage GAN to generate synthetic images with labels for data augmentation, showing that it achieves state-of-the-art results and outperforms single-stage approaches when trained on very few images.
Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method has been evaluated on the segmentation of chest radiographic images, showing promising results. The multistage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach.