The field of medical image segmentation is witnessing significant advancements with the development of innovative models and techniques. Recent research has focused on improving the accuracy, efficiency, and robustness of segmentation models, particularly in the context of 3D images and multi-modal inputs. One notable trend is the integration of neural operators and transformers to capture long-range spatial correlations and improve resolution robustness. Another area of interest is the development of foundation models that can handle incomplete or missing input modalities, enhancing real-world applicability. Furthermore, researchers are exploring the use of diffusion-based models to address annotation variability and provide a more comprehensive understanding of medical images. Noteworthy papers in this area include HNOSeg-XS, which proposes a resolution-robust architecture for 3D image segmentation, and F3-Net, a foundation model designed for full abnormality segmentation of medical images with flexible input modality requirements. Other notable papers include Generalizable 7T T1-map Synthesis, BrainLesion Suite, HANS-Net, Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation, Unified Medical Image Segmentation with State Space Modeling Snake, and DiffOSeg.