TriCSART: Semi-Supervised Medical Image Segmentation With Triple-Level Contrastive Learning and Selective Active...

Semi-supervised medical image segmentation has garnered significant attention due to challenges of limited medical data accessibility and expensive annotation costs. However, existing studies face two critical challenges: 1) while contrastive learning has demonstrated potential in semi-supervised frameworks, prior implementations lack hierarchical modeling, failing to comprehensively integrate contrastive mechanisms across intra-, inter-, and memory bank dimensions; 2) conventional pseudo-labeling strategies inadequately address quality assessment, potentially propagating annotation biases through continual error accumulation. To address these issues, this paper introduces a Triple-Level Contrastive (TLC) Learning and Selective Active Re-Training (SART) strategy for medical image analysis. The proposed method adopts a teacher-student architecture with two main components: the TLC module and the SART module. The TLC module establishes multi-level semantic consistency across different views through three distinct, complementary loss functions, simultaneously enhancing inter-class discriminability and intra-class compactness. To further mitigate sample quality imbalance, the SART module introduces a metric-driven evaluation mechanism to automatically identify salient samples. Finally, these selected unlabeled samples are integrated with labeled data for re-training guided by calculated curriculum scores. Extensive experiments are conducted on five diverse benchmarks, including four public datasets and one private CBCT dataset. The results demonstrate that our approach achieves state-of-the-art performance, consistently outperforming other semi-supervised segmentation strategies. Ablation studies further confirm the efficacy of each proposed component.