Complementary consistency semi-supervised learning for 3D left atrial image segmentation

A network based on complementary consistency training (CC-Net) is proposed for semi-supervised left atrial image segmentation in this paper. From the perspective of complementary information, CC-Net effectively utilizes unlabeled data and resolves the problem that semi-supervised segmentation algorithms currently in use have a limited capacity to extract information from unlabeled data. A primary model and two complementary auxiliary models are part of the complementary symmetric structure of the CC-Net. A complementary consistency training is formed by the inter-model perturbation between the primary model and the auxiliary models. The main model is better able to concentrate on the ambiguous region due to the complementary information provided by the two auxiliary models. Additionally, forcing consistency between the primary model and the auxiliary models makes it easier to obtain decision boundaries with little uncertainty. CC-Net was validated in the benchmark dataset of 2018 left atrial segmentation challenge, reaching Dice of 89.42% with 10% labeled data training and 91.14% with 20% labeled data training. By comparing with current state-of-the-art algorithms, CC-Net has the best segmentation performance and robustness. Our code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.