The recent advancements in medical image segmentation have shown a strong trend towards integrating multi-modal and multi-scale approaches to enhance the accuracy and robustness of segmentation models. Researchers are increasingly leveraging advanced neural network architectures, such as U-Net variants, and incorporating attention mechanisms, graph neural networks, and temporal modeling to capture complex features and dependencies within medical images. These innovations are particularly evident in the segmentation of challenging anatomical structures, such as brain tumors, thyroid nodules, and lymphoid structures, where the integration of contextual and spatial information is crucial for precise delineation. Additionally, the use of self-supervised learning and contrastive learning techniques is emerging as a promising direction for improving image retrieval and guidance in surgical settings, demonstrating the potential to bridge the gap between pre-operative and intra-operative imaging. Overall, the field is progressing towards more sophisticated and context-aware models that not only improve segmentation accuracy but also enhance the interpretability and generalizability of these models across diverse clinical scenarios.