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Primary angle-closure disease (PACD) is a severe retinal disease, which mightcause irreversible vision loss. In clinic, accurate identification ofangle-closure and localization of the scleral spur's position on anteriorsegment optical coherence tomography (AS-OCT) is essential for the diagnosis ofPACD. However, manual delineation might confine in low accuracy and lowefficiency. In this paper, we propose an efficient and accurate end-to-endarchitecture for angle-closure classification and scleral spur localization.Specifically, we utilize a revised ResNet152 as our backbone to improve theaccuracy of the angle-closure identification. For scleral spur localization, weadopt EfficientNet as encoder because of its powerful feature extractionpotential. By combining the skip-connect module and pyramid pooling module, thenetwork is able to collect semantic cues in feature maps from multipledimensions and scales. Afterward, we propose a novel keypoint registration lossto constrain the model's attention to the intensity and location of the scleralspur area. Several experiments are extensively conducted to evaluate our methodon the angle-closure glaucoma evaluation (AGE) Challenge dataset. The resultsshow that our proposed architecture ranks the first place of the classificationtask on the test dataset and achieves the average Euclidean distance error of12.00 pixels in the scleral spur localization task.

Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks