DeepDisc: Optic Disc Segmentation Based on Atrous Convolution and Spatial Pyramid Pooling

Abstract

The optic disc (OD) segmentation is an important step for fundus image base disease diagnosis. In this paper, we propose a novel and effective method called DeepDisc to segment the OD. It mainly contains two components: atrous convolution and spatial pyramid pooling. The atrous convolution adjusts filter’s field-of-view and controls the resolution of features. In addition, the spatial pyramid pooling module probes convolutional features at multiple scales and encodes global context information. Both of them are used to further boost OD segmentation performance. Finally, we demonstrate that our DeepDisc system achieves state-of-the-art disc segmentation performance on the ORIGA and Messidor datasets without any post-processing strategies, such as dense conditional random field.