A network based on complementary consistency training, called CC-Net, has been proposed for semi-supervised left atrium image segmentation. CC-Net efficiently utilizes unlabeled data from the perspective of complementary information to address the problem of limited ability of existing semi-supervised segmentation algorithms to extract information from unlabeled data. The complementary symmetric structure of CC-Net includes a main model and two auxiliary models. The complementary model inter-perturbations between the main and auxiliary models force consistency to form complementary consistency. The complementary information obtained by the two auxiliary models helps the main model to effectively focus on ambiguous areas, while enforcing consistency between the models is advantageous in obtaining decision boundaries with low uncertainty. CC-Net has been validated on two public datasets. In the case of specific proportions of labeled data, compared with current advanced algorithms, CC-Net has the best semi-supervised segmentation performance. Our code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.

翻译：针对半监督左心房图像分割，提出了一种基于互补一致性训练的网络CC-Net。该网络从互补信息的角度有效利用未标记数据，以解决现有半监督分割算法从无标签数据中提取信息能力有限的问题。CC-Net的互补对称结构包含一个主模型和两个辅助模型。主模型与辅助模型之间的互补模型互扰动迫使模型输出一致性，从而形成互补一致性。两个辅助模型获取的互补信息有助于主模型有效聚焦于模糊区域，同时强制模型间的一致性有利于获得低不确定性的决策边界。CC-Net已在两个公开数据集上验证。在特定比例标记数据的情况下，与当前先进算法相比，CC-Net具有最佳半监督分割性能。我们的代码已开源在https://github.com/Cuthbert-Huang/CC-Net。