Semi-supervised segmentation of optic cup in retinal fundus images using variational autoencoder

Abstract

Accurate segmentation of optic cup and disc in retinal fundus images is essential to compute the cup to disc ratio parameter, which is important for glaucoma assessment. The ill-defined boundaries of optic cup makes the segmentation a lot more challenging compared to optic disc. Existing approaches have mainly used fully supervised learning that requires many labeled samples to build a robust segmentation framework. In this paper, we propose a novel semi-supervised method to segment the optic cup, which can accurately localize the anatomy using limited number of labeled samples. The proposed method leverages the inherent feature similarity from a large number of unlabeled images to train the segmentation model from a smaller number of labeled images. It first learns the parameters of a generative model from unlabeled images using variational autoencoder. The trained generative model provides the feature embedding of the images which allows the clustering of the related observation in the latent feature space. We combine the feature embedding with the segmentation autoencoder which is trained on the labeled images for pixel-wise segmentation of the cup region. The main novelty of the proposed approach is in the utilization of generative models for semi-supervised segmentation. Experimental results show that the proposed method successfully segments optic cup with small number of labeled images, and unsupervised feature embedding learned from unlabeled data improves the segmentation accuracy. Given the challenge of access to annotated medical images in every clinical application, the proposed framework is a key contribution and applicable for segmentation of different anatomies across various medical imaging modalities.