Probability distribution guided optic disc and cup segmentation from fundus images

In this paper, we proposed and validated a probability distribution guided network for segmenting optic disc (OD) and optic cup (OC) from fundus images. Uncertainty is inevitable in deep learning, as induced by different sensors, insufficient samples, and inaccurate labeling. Since the input data and the corresponding ground truth label may be inaccurate, they may actually follow some potential distribution. In this study, a variational autoencoder (VAE) based network was proposed to estimate the joint distribution of the input image and the corresponding segmentation (both the ground truth segmentation and the predicted segmentation), making the segmentation network learn not only pixel-wise information but also semantic probability distribution. Moreover, we designed a building block, namely the Dilated Inception Block (DIB), for a better generalization of the model and a more effective extraction of multi-scale features. The proposed method was compared to several existing state-of-the-art methods. Superior segmentation performance has been observed over two datasets (ORIGA and REFUGE), with the mean Dice overlap coefficients being 96.57% and 95.81% for OD and 88.46% and 88.91% for OC.