用卷积神经网络识别AS-OCT图像上的主角闭合

原发性闭角症（PACD）是一种严重的视网膜疾病，可能会导致不可逆的视力丧失。在临床中，在前段光学相干断层扫描（AS-OCT）上准确识别闭角和巩膜骨刺位置的定位对于诊断PACD至关重要。..

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

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.