The field of medical image segmentation is moving towards developing more generalizable and interpretable models. Recent works have focused on addressing the challenges of domain shifts and limited annotated datasets, exploring solutions such as channel regularization and semi-supervised learning with generative adversarial networks. These approaches have shown promise in improving the accuracy and robustness of segmentation models, particularly in applications such as coronary vessel segmentation and early stroke diagnosis. The development of multi-task datasets and benchmarks is also underway, aiming to provide more comprehensive and diverse resources for the research community. Noteworthy papers include AngioDG, which proposes a novel channel-informed feature-modulated single-source domain generalization approach, and Leveraging Unlabeled Scans for NCCT Image Segmentation, which introduces a semi-supervised GAN approach for early ischemic stroke region delineation. Additionally, the LiMT dataset provides a valuable resource for liver image analysis tasks.