The Winner of Age Challenge: Going One Step Further From Keypoint Detection to Scleral Spur Localization

Abstract

Primary angle-closure glaucoma (PACG) is a major sub-type of glaucoma that is responsible for half of the glaucoma-related blindness worldwide. The early detection of PACG is very important, so as to provide timely treatment and prevent potential irreversible vision loss. Clinically, the diagnosis of PACG is based on the evaluation of anterior chamber angle (ACA) with anterior segment optical coherence tomography (AS-OCT). To this end, the Angle closure Glaucoma Evaluation (AGE) challenge1 held on MICCAI 2019 aims to encourage researchers to develop automated systems for angle closure classification and scleral spur (SS) localization. We participated in the competition and won the championship on both tasks. In this paper, we share some ideas adopted in our entry of the competition, which significantly improve the accuracy of scleral spur localization. There are extensive literatures on keypoint detection for the tasks such as human body keypoint and facial landmark detection. However, they are proven to fail on dealing with scleral spur localization in the experiments, due to the gap between natural and medical images. In this regard, we propose a set of constraints to encourage a two-stage keypoint detection framework to spontaneously exploit diverse information, including the image-level knowledge and contextual information around SS, from the AS-OCT for the accurate SS localization. Extensive experiments are conducted to demonstrate the effectiveness of the proposed constraints.1https://age.grand-challenge.org/