Dynamic thresholding and robust contrastive techniques for enhanced semi-supervised cardiac segmentation.

Cardiac segmentation plays a crucial role in the diagnosis of cardiovascular diseases. However, the manual annotation of cardiac structures is a labor-intensive and time-consuming task that requires highly trained experts. Moreover, the availability of labeled data for training segmentation models is often limited due to the challenges associated with acquiring accurate annotations. To address this issue, we propose a novel semi-supervised cardiac segmentation framework that only needs a small set of labeled data with a larger pool of unlabeled data. We propose three strategies: dynamic pseudo-label threshold map, robust entropy minimization and contrastive consistency from the perspective of pseudo-labeling, entropy minimization and consistency regularization. Specifically, we propose a pixel-wise, class-wise and adaptive map to generate threshold maps and use the map for robust entropy minimization to reduce the noise from low-confidence samples. Besides, to utilize the unlabeled data sufficiently, we add contrastive consistency loss to implement regularization. Extensive experiments on the ACDC and MMWHS datasets demonstrate that our method achieves competitive performance compared to state-of-the-art approaches across various labeled data ratios. Ablation studies further validate the effectiveness and robustness of each component. Our framework shows strong potential for accurate diagnosis with limited annotations, and our code will be made publicly available.