CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation

As an important task in medical imaging analysis, automatic lymph node segmentation from computed tomography (CT) scans has been studied well in recent years, but it is still very challenging due to the lack of adequately-labeled training data. Manually annotating a large number of lymph node segmentations is expensive and time-consuming. For this reason, data augmentation can be considered as a surrogate of enriching the data. However, most of the traditional augmentation methods use a combination of affine transformations to manipulate the data, which cannot increase the diversity of the data’s contextual information. To mitigate this problem, this paper proposes a data augmentation approach based on generative adversarial network (GAN) to synthesize a large number of CT-realistic images from customized lymph node masks. In this work, the pix2pix GAN model is used due to its strength for image generation, which can learn the structural and contextual information of lymph nodes and their surrounding tissues from CT scans. With these additional augmented images, a robust U-Net model is learned for lymph node segmentation. Experimental results on NIH lymph node dataset demonstrate that the proposed data augmentation approach can produce realistic CT images and the lymph node segmentation performance is improved effectively using the additional augmented data, e.g. the Dice score increased about 2.2% (from 80.3% to 82.5%).