Decoupled Consistency for Semi-supervised Medical Image Segmentation

By fully utilizing unlabeled data, the semi-supervised learning (SSL) technique has recently produced promising results in the segmentation of medical images. Pseudo labeling and consistency regularization are two effective strategies for using unlabeled data. Yet, the traditional pseudo labeling method will filter out low-confidence pixels. The advantages of both high- and low-confidence data are not fully exploited by consistency regularization. Therefore, neither of these two methods can make full use of unlabeled data. We proposed a novel decoupled consistency semi-supervised medical image segmentation framework. First, the dynamic threshold is utilized to decouple the prediction data into consistent and inconsistent parts. For the consistent part, we use the method of cross pseudo supervision to optimize it. For the inconsistent part, we further decouple it into unreliable data that is likely to occur close to the decision boundary and guidance data that is more likely to emerge near the high-density area. Unreliable data will be optimized in the direction of guidance data. We refer to this action as directional consistency. Furthermore, in order to fully utilize the data, we incorporate feature maps into the training process and calculate the loss of feature consistency. A significant number of experiments have demonstrated the superiority of our proposed method. The code is available at https://github.com/wxfaaaaa/DCNet .