The field of medical image segmentation is rapidly advancing with a focus on semi-supervised learning methods. Recent developments have seen a shift towards leveraging limited labeled data and enforcing consistency between labeled and unlabeled data. This has led to improvements in segmentation accuracy and the ability to handle complex anatomical structures. Notably, the use of teacher-student models, prototype-based cross-contrast consistency, and dual cross-image semantic consistency have shown promising results.

Some noteworthy papers in this area include: SurgPIS, which introduces a unified part-aware instance segmentation model for surgical instruments, achieving state-of-the-art performance in part-aware instance segmentation. The Style-Aware Blending and Prototype-Based Cross-Contrast Consistency framework, which tackles the challenges of separated training data streams and incomplete utilization of supervisory information, demonstrating superiority across multiple medical segmentation benchmarks. The Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling framework, which proposes dual paradigms to encourage region-level semantic consistency and devises a novel self-aware confidence estimation strategy, showing superior segmentation results over previous state-of-the-art approaches.