  Primary angle closure glaucoma (PACG) is the leading cause of irreversible
blindness among Asian people. Early detection of PACG is essential, so as to
provide timely treatment and minimize the vision loss. In the clinical
practice, PACG is diagnosed by analyzing the angle between the cornea and iris
with anterior segment optical coherence tomography (AS-OCT). The rapid
development of deep learning technologies provides the feasibility of building
a computer-aided system for the fast and accurate segmentation of cornea and
iris tissues. However, the application of deep learning methods in the medical
imaging field is still restricted by the lack of enough fully-annotated
samples. In this paper, we propose a novel framework to segment the target
tissues accurately for the AS-OCT images, by using the combination of
weakly-annotated images (majority) and fully-annotated images (minority). The
proposed framework consists of two models which provide reliable guidance for
each other. In addition, uncertainty guided strategies are adopted to increase
the accuracy and stability of the guidance. Detailed experiments on the
publicly available AGE dataset demonstrate that the proposed framework
outperforms the state-of-the-art semi-/weakly-supervised methods and has a
comparable performance as the fully-supervised method. Therefore, the proposed
method is demonstrated to be effective in exploiting information contained in
the weakly-annotated images and has the capability to substantively relieve the
annotation workload.
