Weakly supervised segmentation of retinal layers on OCT images with AMD using uncertainty prototype and boundary regression.

Abstract

Retinal layer segmentation for optical coherence tomography (OCT) images of eyes is a critical step in the diagnosis and treatment of age-related macular degeneration (AMD) eye disease. In recent years, dense annotation supervised OCT layer segmentation methods have made significant progress. However, obtaining pixel-by-pixel labeled masks from OCT retinal images is time-consuming and labor-intensive. To reduce dependence on dense annotations, this paper proposes a novel weakly supervised layer segmentation method with Uncertainty Prototype module and Boundary Regression loss (W-UPBR), which only requires scribble annotations. Specifically, we first propose a feature enhancement U-Net (FEU-Net) to alleviate the severe layer distortion problem in OCT images with AMD. And this model serves as the backbone of a dual-branch network framework to enhance features. Within FEU-Net, in addition to the basic U-Net, two modules have been proposed: the global-local context-aware (GLCA) module, which captures both global and local contextual information, and the multi-scale fusion (MSF) module, designed for fusing multi-scale features. Secondly, we propose an uncertainty prototype module that combines the uncertainty-guided prototype and distance optimization loss. This module aims to exploit the similarities and dissimilarities between OCT images, thereby reducing mis-segmentation in layers caused by interference factors. Furthermore, a mixed pseudo-label strategy is incorporated to mix different predictions to alleviate the limitations posed by insufficient supervision and further promote network training. Finally, we design a boundary regression loss that constrains the boundaries in both 1D and 2D dimensions to enhance boundary under the supervision of generated mixed pseudo-labels, thereby reducing topological errors. The proposed method was evaluated on three datasets, and the results show that the proposed method outperformed other state-of-the-art weakly supervised methods and could achieve comparable performance to fully supervised methods.