Automatic segmentation of fluid in OCT (Optical Coherence Tomography) images is beneficial for ophthalmologists to make an accurate diagnosis. Currently, data-driven convolutional neural networks (CNNs) have achieved great success in OCT fluid segmentation. However, obtaining pixel-level masks of OCT images is time-consuming and requires expertise. The popular weakly-supervised strategy is to generate noisy pseudo-labels from weak annotations, but the noise information introduced may mislead the model training. To address this issue, (i) we propose a superpixel-guided method for generating noisy labels from weak point annotations, called Point to Noisy by Superpixel (PNS), which limits the network from over-fitting noise by assigning low confidence to suspiciously noisy label pixels, and (ii) we propose a Two-Stage Mean-Teacher-assisted Confident Learning (2SMTCL) method based on MTCL for multi-category OCT fluid segmentation, which alleviates the uncertainty and computing power consumption introduced by the real-time characterization noise of MTCL. For evaluation, we have constructed a 2D OCT fluid segmentation dataset. Compared with other state-of-art label-denoising methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation as well as label denoising. Our study provides an efficient, accurate, and practical solution for fluid segmentation of OCT images, which is expected to have a positive impact on the diagnosis and treatment of patients in the field of ophthalmology.

翻译：OCT（光学相干断层扫描）图像中液体的自动分割有助于眼科医生做出准确诊断。当前，数据驱动的卷积神经网络（CNNs）已在OCT液体分割中取得显著成功。然而，获取OCT图像的像素级标注耗时且需要专业知识。流行的弱监督策略是从弱标注生成含噪声的伪标签，但引入的噪声信息可能会误导模型训练。为解决该问题，我们提出：（i）一种基于超像素指导的弱点标注噪声标签生成方法，称为PNS（Point to Noisy by Superpixel），该方法通过为可疑噪声标签像素分配低置信度来限制模型对噪声的过拟合；（ii）基于MTCL的两阶段平均教师辅助置信学习（2SMTCL）方法，用于多类别OCT液体分割，该方法减轻了MTCL实时表征噪声带来的不确定性和计算资源消耗。为进行评估，我们构建了一个二维OCT液体分割数据集。与现有最先进的去噪标签方法相比，综合实验结果表明，所提方法在OCT液体分割及标签去噪方面均能实现优异性能。本研究为OCT图像液体分割提供了一种高效、准确且实用的解决方案，有望对眼科领域患者的诊断与治疗产生积极影响。