Semi-Supervised Medical Image Segmentation Based on Feature Similarity and Multi-Level Information Fusion Consistency

Semantic segmentation is a key task in computer vision, with medical image segmentation as a prominent downstream application that has seen significant advancements in recent years. However, the challenge of requiring extensive annotations in medical image segmentation remains exceedingly difficult. In addressing this issue, semi-supervised semantic segmentation has emerged as a new approach to mitigate annotation burdens. Nonetheless, existing methods in semi-supervised medical image segmentation still face challenges in fully exploiting unlabeled data and efficiently integrating labeled and unlabeled data. Therefore, this paper proposes a novel network model-feature similarity multilevel information fusion network (FSMIFNet). First, the feature similarity module is introduced to harness deep feature similarity among unlabeled images, predicting true label constraints and guiding segmentation features with deep feature relationships. This approach fully exploits deep feature information from unlabeled data. Second, the multilevel information fusion framework integrates labeled and unlabeled data to enhance segmentation quality in unlabeled images, ensuring consistency between original and feature maps for comprehensive optimization of detail and global information. In the ACDC dataset, our method achieves an mDice of 0.684 with 5% labeled data, 0.873 with 10%, 0.884 with 20%, and 0.897 with 50%. Experimental results demonstrate the effectiveness of FSMIFNet in semi-supervised semantic segmentation of medical images, outperforming existing methods on public benchmark datasets. The code and models are available at .