  Primary angle-closure disease (PACD) is a severe retinal disease, which might
cause irreversible vision loss. In clinic, accurate identification of
angle-closure and localization of the scleral spur's position on anterior
segment optical coherence tomography (AS-OCT) is essential for the diagnosis of
PACD. However, manual delineation might confine in low accuracy and low
efficiency. In this paper, we propose an efficient and accurate end-to-end
architecture for angle-closure classification and scleral spur localization.
Specifically, we utilize a revised ResNet152 as our backbone to improve the
accuracy of the angle-closure identification. For scleral spur localization, we
adopt EfficientNet as encoder because of its powerful feature extraction
potential. By combining the skip-connect module and pyramid pooling module, the
network is able to collect semantic cues in feature maps from multiple
dimensions and scales. Afterward, we propose a novel keypoint registration loss
to constrain the model's attention to the intensity and location of the scleral
spur area. Several experiments are extensively conducted to evaluate our method
on the angle-closure glaucoma evaluation (AGE) Challenge dataset. The results
show that our proposed architecture ranks the first place of the classification
task on the test dataset and achieves the average Euclidean distance error of
12.00 pixels in the scleral spur localization task.
