ABSTRACT
Optical coherence tomography (OCT) is widely used for its high resolution. Accurate OCT image segmentation can significantly improve the diagnosis and treatment of retinal diseases such as Diabetic Macular Edema (DME). However, in resource-limited regions, portable devices with low-quality output are more frequently used, severely affecting the performance of segmentation. To address this issue, we propose a novel methodology in this paper, including a dedicated pre-processing pipeline and an end-to-end double U-shaped cascaded architecture, H-Unets. In addition, an Adaptive Attention Fusion (AAF) module is elaborately designed to improve the segmentation performance of H-Unets. To demonstrate the effectiveness of our method, we conduct a bunch of ablation and comparative studies on three open-source datasets. The experimental results show the validity of the pre-processing pipeline and H-Unets, achieving the highest Dice score of 90.60%±0.87% among popular methods in a relatively small model size.