Semi-supervised medical image segmentation via feature similarity and reliable-region enhancement

Semantic segmentation is a crucial task in the field of computer vision, and medical image segmentation, as its downstream task, has made significant breakthroughs in recent years. However, the issue of requiring a large number of annotations in medical image segmentation has remained a major challenge. Semi-supervised semantic segmentation has provided a powerful approach to address the annotation problem. Nevertheless, existing semi-supervised semantic segmentation methods in medical images have drawbacks, such as insufficient exploitation of unlabeled data information and inefficient utilization of all pseudo-label information. We introduces a novel segmentation model, the Feature Similarity and Reliable-region Enhancement Network (FSRENet), to overcome these limitations. Firstly, this paper proposes a Feature Similarity Module (FSM), which combines the dense feature prediction ability of true labels for unlabeled images with segmentation features as additional constraints, utilizing the similarity relationship between dense features to constrain segmentation features, and thus fully exploiting the dense feature information of unlabeled data. Additionally, the Reliable-region Enhancement Module (REM) designs a high-confidence network structure by fusing two networks that can learn from each other, forming a triple-network structure. The high-confidence network generates reliable pseudo-labels that further constrain the predictions of the two networks, achieving the goal of enhancing the weight of reliable regions, reducing the noise interference of pseudo-labels, and efficiently utilizing all pseudo-label information. Experimental results on the ACDC and LA datasets demonstrate that the FSRENet model proposed in this paper excels in the task of semi-supervised semantic segmentation of medical images and outperforms the majority of existing methods. Our code is available at: https://github.com/gdghds0/FSRENet-master.