MCC-Net: Mamba based Consistency Constraints Network for Semi-Supervised 3D Medical Image Segmentation

Abstract

Semi-supervised learning methods combine a small amount of labeled data with a large amount of unlabeled data to achieve high-precision segmentation while reducing the annotation cost. However, existing semi-supervised learning methods usually focus on data-level perturbations or improvements in network structures, ignoring the problem of insufficient information interaction between different branches and regions in complex texture tasks. In addition, in scenarios that require high-resolution processing (such as 3D medical images), traditional Transformer-based methods are not only computationally expensive but also prone to overfitting problems. Therefore, to address the above problems, we propose a Mamba based Consistency Constraints network (MCC-Net) for semi-supervised 3D medical image segmentation. Specifically, the model is organized by a shared encoder and three-branch decoder architecture. First, a consistency regularization constraint mechanism is introduced to combine the segmentation map of one decoder with the pseudo-labels of other decoders, thereby capturing more valuable features in high-uncertainty areas and generating stable, low-entropy predictions; second, a new consistency loss function is designed to construct constraints between the signed distance map (SDM) of the two auxiliary decoders and the segmentation map of the main decoder to enhance the learning ability of the target geometric structure. In addition, a Fusion Mamba(FM) Block is proposed to improve the model’s capabilities in deep semantic feature extraction and computational efficiency by modeling long-distance dependent features. Experimental results on public dataset show that, compared with six state-of-the-art semi-supervised segmentation methods, our method achieves Dice scores of 89.48%, 91.46% and 91.96%, respectively, when 10%, 20% and 30% of labeled data are used for training, significantly outperforming the other methods. The experimental results show that the model has strong advantages in both segmentation accuracy and utilization of unlabeled data.