Self-Guided Optimization Semi-Supervised Method for Joint Segmentation of Macular Hole and Cystoid Macular Edema in Retinal OCT Images

Macular hole (MH) and cystoid macular edema (CME) are two common retinal pathologies that cause vision loss. Accurate segmentation of MH and CME in retinal OCT images can greatly aid ophthalmologists to evaluate the relevant diseases. However, it is still challenging as the complicated pathological features of MH and CME in retinal OCT images, such as the diversity of morphologies, low imaging contrast, and blurred boundaries. In addition, the lack of pixel-level annotation data is one of the important factors that hinders the further improvement of segmentation accuracy. Focusing on these challenges, we propose a novel self-guided optimization semi-supervised method termed Semi-SGO for joint segmentation of MH and CME in retinal OCT images. Aiming to improve the model's ability to learn the complicated pathological features of MH and CME, while alleviating the feature learning tendency problem that may be caused by the introduction of skip-connection in U-shaped segmentation architecture, we develop a novel dual decoder dual-task fully convolutional neural network (D3T-FCN). Meanwhile, based on our proposed D3T-FCN, we introduce a knowledge distillation technique to further design a novel semi-supervised segmentation method called Semi-SGO, which can leverage unlabeled data to further improve the segmentation accuracy. Comprehensive experimental results show that our proposed Semi-SGO outperforms other state-of-the-art segmentation networks. Furthermore, we also develop an automatic method for measuring the clinical indicators of MH and CME to validate the clinical significance of our proposed Semi-SGO. The code will be released on Github 1,2.