The field of medical image analysis is rapidly advancing, with a focus on developing more accurate and efficient segmentation methods. Recent research has highlighted the importance of leveraging shape priors, anatomical knowledge, and multimodal interactions to improve model performance. The use of synthetic data, few-shot learning, and transfer learning has also shown promise in addressing the challenges of data scarcity and domain adaptation. Furthermore, the development of novel architectures and techniques, such as vision-language multimodal systems and competitive query refinement, is enabling more accurate and robust segmentation of complex anatomical structures. Notable papers in this area include VessShape, which demonstrates the effectiveness of pre-training with shape priors for blood vessel segmentation, and SpinalSAM-R1, which introduces a multimodal vision-language system for spine CT segmentation. Additionally, VesSAM and MIQ-SAM3D showcase innovative approaches to vessel segmentation and multi-instance segmentation, respectively. Overall, these advances are paving the way for more accurate and efficient medical image analysis, with potential applications in disease diagnosis, treatment planning, and surgical training.