Macular edema is the accumulation of fluid leakage from retinal capillaries. Optical coherence tomography (OCT) images can show changes in the retinal tissue caused by ophthalmological diseases, such as fluid accumulation. Therefore, the segmentation of retinal fluid is important. Some methods based on image processing and machine learning often require large amounts of labeled data and rich domain knowledge. This study proposes a self-ensembling semi-supervised model based on uncertainty guidance, namely, UGNet. The model is trained end-to-end with a few labeled data and plenty of unlabeled data, and contains a teacher model and a student model with the same architecture. The two models consist of an encoder and three decoders, which are used to predict the probability map, contour map, and distance map. The segmentation result is the fusion result of the three maps generated by the student model. The selective kernel module (SKM) is embedded in the decoder to make the model adaptively adjust the receptive field according to the size of the fluid. The uncertainty of teacher model evaluation guides the student model to learn more reliable knowledge. The proposed method is trained and evaluated on the RETOUCH challenge dataset. The experimental results show that our method achieves better segmentation results than other start-of-the-art methods.

中文翻译：

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光学相干断层扫描图像中多类视网膜液半监督分割的不确定性引导自组装模型

黄斑水肿是视网膜毛细血管渗漏液的积聚。光学相干断层扫描 (OCT) 图像可以显示由眼科疾病引起的视网膜组织变化，例如积液。因此，视网膜液的分割很重要。一些基于图像处理和机器学习的方法往往需要大量的标记数据和丰富的领域知识。本研究提出了一种基于不确定性指导的自组装半监督模型，即UGNet。该模型使用少量标记数据和大量未标记数据进行端到端训练，并包含具有相同架构的教师模型和学生模型。这两个模型由一个编码器和三个解码器组成，用于预测概率图、等高线图和距离图。分割结果是学生模型生成的三张图的融合结果。解码器中嵌入了选择性内核模块（SKM），使模型根据流体的大小自适应地调整感受野。教师模型评价的不确定性引导学生模型学习更可靠的知识。所提出的方法在 RETOUCH 挑战数据集上进行训练和评估。实验结果表明，我们的方法比其他最先进的方法取得了更好的分割结果。所提出的方法在 RETOUCH 挑战数据集上进行训练和评估。实验结果表明，我们的方法比其他最先进的方法取得了更好的分割结果。所提出的方法在 RETOUCH 挑战数据集上进行训练和评估。实验结果表明，我们的方法比其他最先进的方法取得了更好的分割结果。