Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achieving robust generalization and domain adaptation remains a significant hurdle, particularly given the resource-intensive nature of some proposed models and their reliance on domain expertise. This survey explores cutting-edge advancements in medical image segmentation, focusing on methodologies such as Generative AI, Few-Shot Learning, Foundation Models, and Universal Models. These approaches offer promising solutions to longstanding challenges. We provide a comprehensive overview of the theoretical foundations, state-of-the-art techniques, and recent applications of these methods. Finally, we discuss inherent limitations, unresolved issues, and future research directions aimed at enhancing the practicality and accessibility of segmentation models in medical imaging. We are maintaining a \href{https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation}{GitHub Repository} to continue tracking and updating innovations in this field.

翻译：医学影像是现代医疗的基石，推动着诊断、治疗规划和患者护理的进步。在其各项任务中，分割仍是最具挑战性的问题之一，这归因于数据可及性、标注复杂性、结构变异性、医学影像模态差异以及隐私限制等多重因素。尽管近期取得进展，实现稳健的泛化与域适应仍是重大障碍，尤其考虑到部分现有模型的资源密集特性及其对领域专业知识的依赖。本综述探讨医学图像分割的前沿进展，重点关注生成式人工智能、少样本学习、基础模型与通用模型等方法论。这些方法为长期存在的挑战提供了有前景的解决方案。我们全面概述了这些方法的理论基础、尖端技术及最新应用。最后，我们讨论了固有局限性、未解决问题以及未来研究方向，旨在提升医学影像分割模型的实用性与可及性。我们维护着\href{https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation}{GitHub代码库}以持续追踪并更新该领域的创新成果。