An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation

Abstract The major challenge in applying deep neural network techniques in the medical imaging domain is how to cope with small datasets and the limited amount of annotated samples. Data augmentation procedures that include conventional geometrical transformation based augmentation techniques and the recent image synthesis techniques using generative adversarial networks (GANs) can be employed to artificially increase the number of training images. This paper is focused on data augmentation for image segmentation task, which has an inherent challenge when compared to the conventional image classification task, due to its requirement to produce a corresponding mask for each generated image. To tackle the challenge of image-mask pair augmentation for image segmentation, this paper proposes a novel two-stage generative adversarial network. The proposed approach first employs a GAN to generate a synthesized binary mask, then incorporates this synthesized mask into the second GAN to perform a conditional generation of the synthesized image. Thus, these two GANs collaborate to generate the synthesized image-mask pairs, which are used to improve the performance of the conventional image segmentation approaches. The proposed approach is evaluated using the cell nuclei image segmentation task and demonstrates the superior performance to outperform both the traditional augmentation methods and the existing GAN-based augmentation methods in extensive results conducted using the benchmark Kaggle cell nuclei image segmentation dataset.