Multi-Perturbation Consistency Learning for Semi-Supervised Medical Image Segmentation

Existing semi-supervised learning (SSL) methods primarily rely on consistency learning to enhance model performance. However, most current approaches only validate the effectiveness of consistency learning under single perturbations, while introducing multiple perturbations may lead to the failure of consistency learning and degrade model performance. To address this issue and effectively leverage multiple perturbations for consistency learning, we propose a semi-supervised medical image segmentation method based on multi-perturbation consistency learning. Specifically, we design a cross-teaching framework integrating sparsely annotated 3D and 2D networks, introducing network perturbations through multidimensional architectures while combining strong and weak data augmentation techniques to achieve input perturbations. Furthermore, to address the instability issue in multi-perturbation consistency learning, we develop two complementary uncertainty-aware correction algorithm targeting labeled and unlabeled data. These designs effectively enhance the model's robustness to both labeled and unlabeled data, overcoming the instability problem in multi-perturbation consistency learning. To validate the proposed method, we conducted experiments on four datasets(ProstateX, HPH55, ACDC, and LA). Experimental results demonstrate that our algorithm outperforms existing methods across all validation datasets and exhibits strong generalization capabilities. This indicates that our approach can maintain excellent performance with limited annotated data while achieving efficient medical image segmentation.The project code will be made publicly available upon acceptance.