A Semi-Supervised Framework for Cardiac MRI Segmentation via Multi-Constraint Collaborative Self-Training

To address the challenges of limited annotated data and the adverse impact of noisy pseudolabels on model generalization in cardiac magnetic resonance imaging (MRI) segmentation, a novel semi-supervised segmentation framework based on multi-constraint collaborative self-training was developed.The proposed approach integrates uncertainty-guided pseudolabel quality control, prototype-driven inter-and intra-class consistency constraints, and a multi-scale adversarial learning mechanism.During each iteration of self-training, a dynamic selection of pseudo-labels generated by unlabeled samples was conducted using a method that fuses confidence and entropy-based uncertainty quantification, thereby enhancing the reliability of the pseudo-supervision signals.Simultaneously, class-specific prototype vectors were dynamically maintained to enforce explicit constraints that encourage intra-class feature aggregation and inter-class feature separability, improving the discriminative capacity of the feature space.In addition, both global and local discriminators were introduced to impose dual-level quality constraints on the global morphology and local structural details of the segmentation outputs, resulting in refined boundary delineation and enhanced structural consistency.Extensive experiments conducted on the publicly available ACDC dataset demonstrated that the proposed method achieved an average Dice Similarity Coefficient (DSC) of 0.730 with only 5% of the annotations, outperforming existing methods such as Deep Co-Training (DCT) (0.711) and Mean Teacher (MT) (0.654) and approaching the performance of full supervision (0.891).When the annotation ratio was increased to 10%, the average DSC further improved to 0.801, consistently surpassing all comparative methods.Ablation studies confirmed the effectiveness of each key module.This study provides an efficient and robust solution for automatic segmentation of medical images in low-resource scenarios, offering promising potential for real-world clinical applications.