Complementary consistency semi-supervised learning for 3D left atrial image segmentation
This work addresses the challenge of medical image segmentation with limited labeled data, which is incremental as it builds on existing semi-supervised methods.
The paper tackles the problem of limited ability in semi-supervised segmentation algorithms to extract information from unlabeled data for 3D left atrial image segmentation, proposing CC-Net which achieves the best semi-supervised segmentation performance compared to current advanced algorithms on two public datasets.
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.