VCLIPSeg: Voxel-Wise CLIP-Enhanced Model for Semi-supervised Medical Image Segmentation

Semi-supervised learning has emerged as a critical approach for addressing medical image segmentation with limited annotation, and pseudo labeling-based methods made significant progress for this task. However, the varying quality of pseudo labels poses a challenge to model generalization. In this paper, we propose a Voxel-wise CLIP-enhanced model for semi-supervised medical image Segmentation (VCLIPSeg). Our model incorporates three modules: Voxel-Wise Prompts Module (VWPM), Vision-Text Consistency Module (VTCM), and Dynamic Labeling Branch (DLB). The VWPM integrates CLIP embeddings in a voxel-wise manner, learning the semantic relationships among pixels. The VTCM constrains the image prototype features, reducing the impact of noisy data. The DLB adaptively generates pseudo-labels, effectively leveraging the unlabeled data. Experimental results on the Left Atrial (LA) dataset and Pancreas-CT dataset demonstrate the superiority of our method over state-of-the-art approaches in terms of the Dice score. For instance, it achieves a Dice score of 88.51% using only 5% labeled data from the LA dataset.