Retinal Vessel Segmentation Under Extreme Low Annotation: A Gan Based Semi-Supervised Approach

Contemporary deep learning based medical image segmentation algorithms require hours of annotation labor by domain experts. These data hungry deep models perform sub-optimally in the presence of limited amount of labeled data. In this paper, we present a data efficient learning framework using the recent concept of Generative Adversarial Networks; this allows a deep neural network to perform significantly better than its fully supervised counterpart in low annotation regime. The proposed method is an extension of our previous work with the addition of a new unsupervised adversarial loss and a structured prediction based architecture. Though generic, we demonstrate the efficacy of our approach for retinal blood vessels segmentation from fundus images on DRIVE and STARE datasets. We experiment with extreme low annotation budget and we show, that under this constrained data setting, the proposed method outperforms our previous method and other fully supervised benchmark models. In addition, our systematic ablation studies suggest some key observations for successfully training GAN based semi-supervised algorithms with an encoder-decoder style network architecture.