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）图像中液体的自动分割有助于眼科医生做出准确诊断。尽管半监督OCT液体分割网络通过引入额外未标注数据提升了性能，但其性能提升仍有限。为解决这一问题，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），该网络能从少量全标注数据和大量点标注数据中学习OCT液体分割。具体而言，我们采用点标注方式对未标注OCT图像中的液体区域进行标注，并通过超像素引导伪标签生成模块（SGPLG）从点标注中生成伪标签及像素级标签置信图。标签置信图可指示伪标签的可靠性。进一步，我们提出置信学习引导标签细化模块（CLGLR），该模块能识别伪标签中的错误信息并实现后续细化。在RETOUCH数据集上的实验表明，本方法可将全标注数据需求降低94.22%，与最优全监督基线的平均交并比差距缩小至仅2%。此外，我们构建了私有二维OCT液体分割数据集进行验证。综合实验结果表明，与其他方法相比，本方法在OCT液体分割任务中能取得优异性能。