Peripapillary Atrophy Segmentation in Fundus Images via Multi-task Learning

Peripapillary atrophy (PPA), a type of aberrant retinal symptom frequently present in older individuals or people with myopia, might indicate the severity of glaucoma or myopia. It is particularly beneficial for diagnosis when PPA is segmented effectively in fundus images. Deep learning is now frequently used for PPA segmentation. However, previous segmentation algorithms frequently mix up PPA with its neighboring tissue, the optic disc (OD), and generate the incorrect PPA area even though PPA is not present in the fundus image. To address these problems, we propose an improved segmentation network based on multi-task learning by combining detection and segmentation of PPA. We analyze the shortcomings of widely used loss functions and define a modified one to guide the training process of the network. We design a three-class segmentation task by introducing the information of OD, forcing the network to learn the difference of characteristics between OD and PPA. Evaluation on a clinical dataset shows that our method achieves an average Dice coefficient of 0.8854 in PPA segmentation, outperforming UNet and TransUNet, two state-of-the-art methods, by 24.4% and 10.6%, respectively.