Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited fully-annotated data and abundant point-annotated data. Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations. The label trust maps provide an indication of the reliability of the pseudo-labels. Furthermore, we propose the Confident Learning Guided Label Refinement (CLGLR) module identifies error information in the pseudo-labels and leads to further refinement. Experiments on the RETOUCH dataset show that we are able to reduce the need for fully-annotated data by 94.22\%, closing the gap with the best fully supervised baselines to a mean IoU of only 2\%. Furthermore, We constructed a private 2D OCT fluid segmentation dataset for evaluation. Compared with other methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation.

翻译：光学相干断层扫描（OCT）图像中积液的自动分割有助于眼科医生做出准确诊断。尽管半监督OCT积液分割网络通过引入额外未标注数据提升了性能，但其性能提升仍存在局限性。为此，我们提出基于教师-学生架构的超像素与置信学习引导点标注网络（SCLGPA-Net），该网络能够从少量完全标注数据和大量点标注数据中学习OCT积液分割。具体而言，我们使用点标注来标注未标注OCT图像中的积液区域，并通过超像素引导伪标签生成（SGPLG）模块从点标注中生成伪标签和像素级标签置信度图。标签置信度图提供了伪标签可靠性的指示信息。此外，我们提出置信学习引导标签精化（CLGLR）模块，用于识别伪标签中的错误信息并实现进一步精化。在RETOUCH数据集上的实验表明，我们能够将完全标注数据的需求减少94.22%，将最优全监督基线方法的平均交并比差距缩小至仅2%。同时，我们构建了私有二维OCT积液分割数据集进行验证。综合实验结果表明，与其他方法相比，所提方法在OCT积液分割任务中能够取得优异性能。