Automatic Segmentation of Hemorrhages in the Ultra-Wide Field Retina: Multi-Scale Attention Subtraction Networks and an Ultra-Wide Field Retinal Hemorrhage Dataset

Abstract

Ultra-wide field (UWF) retinal imaging can improve the detection rate of retinal hemorrhage as compared with conventional fundus images. However, hemorrhages in UWF retinal images can also become smaller and more widely distributed, which can be time consuming and labor intensive. With the development of computer technology, automatic segmentation techniques can assist physicians in diagnosis. However, the lack of publicly available UWF retinal hemorrhage segmentation datasets has limited the development of automatic hemorrhage segmentation techniques in UWF retinal images. We present a large-scale high-quality UWF retinal hemorrhage segmentation dataset, named UWF-RHS Dataset, for public use. To the best of our knowledge, we are the first team to make the UWF retinal hemorrhage segmentation dataset publicly available. In addition, we propose a multi-scale attention subtraction network (MASNet) for UWF retinal hemorrhage segmentation. Specifically, highly focused lesion features are extracted by using the proposed multi-scale attention subtraction (MAS) module at the progress of the skip-connection. Several comparative experiments and ablation experiments were performed at the UWF-RHS Dataset, and all experiments state that our proposed method is effective in diagnosing retinal hemorrhages with state-of-the-art results. The proposed UWF-RHS dataset and MASNet will greatly facilitate the development of automated segmentation techniques for UWF retinal hemorrhages.