Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways, (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97.75\% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 4\%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.

翻译：医学图像分割标注因医学图像中分割对象与背景的模糊边界而存在专家间的标注差异。尽管在完全监督学习中利用每张图像的多个标注进行深度模型训练已被广泛研究，但由于分割标注需要大量时间和人力成本，获取大规模多标注数据极具挑战性，导致大多数图像缺乏标注。针对这一问题，我们提出多标注半监督集成网络（MSE-Nets），用于从有限的多标注数据和丰富的无标注数据中学习分割。具体而言，我们引入网络成对一致性增强（NPCE）模块和多网络伪监督（MNPS）模块，通过考虑两个关键因素来增强MSE-Nets的分割性能：（1）为优化所有可获取多标注数据的利用效率，NPCE在像素级别分离多标注数据的（非）一致性标注，并对一致性标注与不一致性标注采用差异化处理策略；（2）为缓解不精确伪标签的引入问题，MNPS通过利用无标注数据中的一致性伪标签扩展训练数据。最后，我们通过对基础网络的预测取平均来改善置信度校准。在ISIC数据集上的实验表明，我们将多标注数据的需求量降低了97.75%，与最佳全监督基线方法的差值仅为4%的Jaccard指数。此外，与仅依赖单一标注或组合融合方法的其他半监督方法相比，在ISIC和RIGA数据集上的全面实验结果证明，我们提出的方法在边界模糊的医学图像分割中具有优越性能。