Parasitic GAN for Semi-Supervised Brain Tumor Segmentation

In semantic segmentation, researchers face the shortage of pixel-level annotated data. And it is particularly severe in the medical images. On the other hand, the unlabeled data are abundantly produced in the diagnosis routine. In the paper, we introduced the Parasitic GAN for the brain tumor segmentation to exploit the unlabeled data more efficiently. Parasitic GAN is composed of three parts: the segmentor ${\mathcal{S}}$, the generator ${\mathcal{G}}$, and the discriminator ${\mathcal{D}}$. With the label maps produced by the segmentor and the supplementary label maps synthesized by the generator, the discriminator could learn a more precise boundary of ground truth. Thus, the segmentor benefits from the adversarial learning mechanism and the extra supervision provided by the discriminator. This parasitic relationship between the segmentor and the generative adversarial network (${\mathcal{G}}$ and ${\mathcal{D}}$) restricts the fitness ability of the segmentor and improves its generalization capacity. In practice, it definitely improved the performance of segmentor in brain tumor segmentation tasks, increasing the dice score 0.010-0.035.