医学影像分割的最新进展：综述
Recent Advances in Medical Imaging Segmentation: A Survey
AI总结：
本文综述医学影像分割的最新方法，重点梳理生成式AI、少样本学习、基础模型与通用模型的理论、技术、应用、局限及未来方向。
摘要：
医学影像是现代医疗保健的基石，推动着诊断、治疗规划和患者护理的进步。在其各类任务中，由于数据可获取性、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因素，分割仍是最具挑战性的问题之一。尽管近期取得了进展，但实现稳健的泛化和领域适应仍是重大障碍，尤其是考虑到部分所提出模型的资源密集型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AI、少样本学习、基础模型和通用模型等方法。这些方法为长期存在的挑战提供了富有前景的解决方案。我们全面概述了这些方法的理论基础、最新技术以及近期应用。最后，我们讨论了固有局限、尚未解决的问题和未来研究方向，旨在提升医学影像分割模型的实用性和可及性。我们正在维护一个GitHub仓库，以持续追踪和更新该领域的创新。
英文摘要：
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} to continue tracking and updating innovations in this field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
