Liu Xiaoming, Zhang Jia, Zhang Ying, Chen Li, Luo Liangfu, Tang Jinshan
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China.
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China.
Med Image Anal. 2025 May;102:103572. doi: 10.1016/j.media.2025.103572. Epub 2025 Mar 29.
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.
用于眼睛光学相干断层扫描（OCT）图像的视网膜层分割是年龄相关性黄斑变性（AMD）眼病诊断和治疗中的关键步骤。近年来，密集标注监督的OCT层分割方法取得了显著进展。然而，从OCT视网膜图像中获取逐像素标记的掩码既耗时又费力。为了减少对密集标注的依赖，本文提出了一种带有不确定性原型模块和边界回归损失的新型弱监督层分割方法（W-UPBR），该方法仅需要涂鸦标注。具体而言，我们首先提出了一种特征增强U-Net（FEU-Net）来缓解患有AMD的OCT图像中严重的层失真问题。并且该模型作为双分支网络框架的主干来增强特征。在FEU-Net中，除了基本的U-Net之外，还提出了两个模块：全局-局部上下文感知（GLCA）模块，用于捕获全局和局部上下文信息；以及多尺度融合（MSF）模块，用于融合多尺度特征。其次，我们提出了一个不确定性原型模块，它结合了不确定性引导的原型和距离优化损失。该模块旨在利用OCT图像之间的异同，从而减少由干扰因素导致的层内误分割。此外，还引入了一种混合伪标签策略来混合不同的预测，以减轻监督不足带来的限制并进一步促进网络训练。最后，我们设计了一种边界回归损失，在生成的混合伪标签的监督下在一维和二维维度上约束边界，从而减少拓扑错误。所提出的方法在三个数据集上进行了评估，结果表明该方法优于其他现有的弱监督方法，并且可以实现与全监督方法相当的性能。
