Multi-Task Dual Boundary Aware Network for Retinal Layer Segmentation

Layer segmentation of Optical Coherence Tomography (OCT) images is an important step in diagnosing retinal diseases. However, the presence of some artifacts and noise in OCT images often leads to unsatisfactory layer segmentation results. Especially when the number of layers to be segmented is particularly large, the boundaries between layers are indistinguishable, which poses a great challenge to automatic and accurate segmentation. To solve these problems, we propose a novel multi-task dual boundary-aware network to improve the retinal layer segmentation performance in OCT images. Specifically, based on the hierarchical relationship between retinal layers, we design a dual boundary representation method to encode the bidirectional boundary information between layers. Then we design a multi-task architecture and a novel consistency loss to utilize the boundary representation to make the segmentation more accurate. For evaluation, we have built a large-scale OCT layer segmentation dataset with 1,200 images. The comprehensive experimental results show that our method achieves superior performance over other state-of-the-art algorithms.