  Irreversible visual impairment is often caused by primary angle-closure
glaucoma, which could be detected via Anterior Segment Optical Coherence
Tomography (AS-OCT). In this paper, an automated system based on deep learning
is presented for angle-closure detection in AS-OCT images. Our system learns a
discriminative representation from training data that captures subtle visual
cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is
proposed to formulate this learning, which utilizes three particular AS-OCT
regions based on clinical priors: the global anterior segment structure, local
iris region, and anterior chamber angle (ACA) patch. In our method, a sliding
window based detector is designed to localize the ACA region, which addresses
ACA detection as a regression task. Then, three parallel sub-networks are
applied to extract AS-OCT representations for the global image and at
clinically-relevant local regions. Finally, the extracted deep features of
these sub-networks are concatenated into one fully connected layer to predict
the angle-closure detection result. In the experiments, our system is shown to
surpass previous detection methods and other deep learning systems on two
clinical AS-OCT datasets.
