# Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network

## TL;DR

This paper introduces a multi-level deep learning system for automatic angle-closure detection in AS-OCT images, leveraging clinical priors and local region analysis to improve accuracy over existing methods.

## Contribution

The paper proposes a novel Multi-Level Deep Network that integrates global and local AS-OCT features for improved angle-closure detection, utilizing a sliding window for ACA localization.

## Key findings

- Outperforms previous detection methods on clinical datasets.
- Effectively captures subtle visual cues through deep learning.
- Utilizes clinical priors for targeted feature extraction.

## Abstract

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.