The field of medical image segmentation and domain adaptation is rapidly advancing, with a focus on developing more accurate and efficient models. Recent research has explored the use of novel architectures, such as the ACM-UNet and DSSAU-Net, which integrate pretrained CNNs and Mamba models to improve segmentation performance. Additionally, techniques like Shuffle PatchMix augmentation and Decoupled Competitive Framework have been proposed to enhance domain adaptation and semi-supervised learning. The Segment Anything Model (SAM) has also been extensively studied, with modifications like Hierarchical Self-Prompting SAM and GaRA-SAM aiming to improve its robustness and generalization. Notably, SAM-aware Test-Time Adaptation has been introduced as a promising approach for universal medical image segmentation.

Some noteworthy papers include: ACM-UNet, which achieves state-of-the-art performance on the Synapse and ACDC benchmarks with a simple UNet-like design. UST-RUN framework, which leverages intermediate domain information to facilitate knowledge transfer in mixed domain semi-supervised medical image segmentation and improves Dice score by 12.94% on the Prostate dataset. GaRA-SAM, which significantly outperforms prior work on robust segmentation benchmarks, surpassing the previous best IoU score by up to 21.3% on ACDC. DSSAU-Net, which wins the fourth place on the tasks of classification and segmentation in the MICCAI IUGC 2024 competition. SAM-TTA, which establishes a new paradigm for universal medical image segmentation by preserving the generalization of SAM while improving its segmentation performance in medical imaging via a test-time framework.