Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case

Convolutional Neural Network(CNN) based semantic segmentation require extensive pixel level manual annotation which is daunting for large microscopic images. The paper is aimed towards mitigating this labeling effort by leveraging the recent concept of generative adversarial network(GAN) wherein a generator maps latent noise space to realistic images while a discriminator differentiates between samples drawn from database and generator. We extend this concept to a multi task learning wherein a discriminator-classifier network differentiates between fake/real examples and also assigns correct class labels. Though our concept is generic, we applied it for the challenging task of vessel segmentation in fundus images. We show that proposed method is more data efficient than a CNN. Specifically, with 150K, 30K and 15K training examples, proposed method achieves mean AUC of 0.962, 0.945 and 0.931 respectively, whereas the simple CNN achieves AUC of 0.960, 0.921 and 0.916 respectively.