DHANet: An Encoder–Decoder Network With Multiscale Features Fusing for Optic Disk Segmentation

Automatic and accurate segmentation of the optic disk (OD) region has practical applications in the medical field. In this study, a novel encoder-decoder network is proposed to segment the ODs automatically and accurately. The encoder consists of three parts: 1) low-level feature extraction module composed of a dense connectivity block (Dense Block) which can output rich low-level features; 2) high-resolution block (HR Block) which can extract sufficient semantic information while reducing parameters; and 3) atrous spatial pyramid pooling (ASPP) module is used to obtain high-level features. Therefore, the network is named DHANet. The proposed decoder takes advantage of the multiscale features from the encoder to predict OD regions. By comparing it with existing classic models such as U-Net, CE-Net, and DeepLabv3+, as well as the latest excellent U-Net++, Attention U-Net, and CrackSegNet, it has been proven that the proposed method can generally achieve better segmentation performance at a lower cost. The ablation studies proved the influence of each module on the segmentation performance and explained the network structure reasonably. In the case of fewer network parameters, DHANet achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC), and other evaluation metrics. DHANet is lightweight relatively and can use multiscale features to predict OD regions.