The field of medical image segmentation is moving towards developing more robust and generalizable models that can handle domain shifts and out-of-distribution noise patterns. Researchers are exploring innovative approaches such as topology-enhanced test-time adaptation, scale equivariance, and federated learning with dynamic global intensity non-linear augmentation to improve the accuracy and reliability of segmentation models. Noteworthy papers in this area include TopoTTA, which proposes a test-time adaptation framework for tubular structure segmentation, and FedGIN, which enables multimodal organ segmentation without sharing raw patient data. Other notable works focus on augmentation-based domain generalization and joint training from multiple source domains for whole heart segmentation, as well as robust image denoising with scale equivariance.