Multi-scale reciprocal consistency learning for semi-supervised left atrial segmentation

Accurate and enough labels are crucial to constructing an excellent deep-learning framework in processing medical scans. However, the expensive cost of medical labels greatly impedes this process. In this case, semi-supervised learning has shown great potential due to its efficient use of unlabeled data. Therefore, we present a novel multi-scale reciprocal consistency network (RC-NET) to utilize the unlabeled data more efficiently for more accurate 3D left atrial segmentation. Our model consists of a common encoder and two independent decoders with inconsistent up-sampling. The decoders generate feature maps of hidden layers at different resolutions during the up-sampling process. Lower-resolution features typically contain local and detailed information, while higher-resolution features involve global or abstract information. Subsequently, we applied consistency learning to these feature maps. By combining local and global semantic information, the model can obtain a comprehensive understanding of the segmentation targets. The experimental data indicate the MRC-Net outperforms many semi-supervised learning methods. It achieves more accurate segmentation results by efficiently utilizing unlabeled data. This provides a completely new approach to improving semi-supervised learning.