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:提出了一种基于互补一致性训练的网络 CC-Net,用于进行半监督的左心房图像分割。CC-Net 通过互补信息的角度有效地利用未标记数据,以解决现有半监督分割算法从未标记数据中提取信息能力有限的问题。CC-Net 的互补对称结构包括一个主模型和两个辅助模型。互补模型中的互扰作用促使主模型和辅助模型的互补一致性得以形成。两个辅助模型获得的互补信息有助于主模型有效地聚焦于模糊区域,而强制模型之间的一致性有助于获得具有低不确定性的决策边界。CC-Net 已在两个公共数据集上进行了验证。在标记数据比例特定的情况下,与当前先进算法相比,CC-Net 具有最好的半监督分割性能。我们的代码公开可用:https://github.com/Cuthbert-Huang/CC-Net.