Semi-supervised peripapillary atrophy segmentation with shape constraint

Abstract

Peripapillary atrophy (PPA) is a clinical abnormality related to many eye diseases, such as myopia and glaucoma. The shape and area of PPA are essential indicators of disease progression. PPA segmentation is a challenging task due to blurry edge and limited labeled data. In this paper, we propose a novel semi-supervised PPA segmentation method enhanced by prior knowledge. In order to learn shape information in the network, a novel shape constraint module is proposed to restrict the PPA appearance based on active shape model. To further leverage large amount of unlabeled data, a Siamese-like model updated by exponential moving average is introduced to provide pseudo labels. The pseudo labels are further refined by region connectivity correction. Extensive experiments on a clinical dataset demonstrate that our proposed PPA segmentation method provides good qualitative and quantitative performance.