综述医学影像分割前沿，聚焦生成AI与少样本学习等新方法。
Recent Advances in Medical Imaging Segmentation: A Survey
- 整合生成AI、少样本学习等新技术应对标注难、泛化差问题。
- 梳理主流模型理论基础与应用效果，揭示当前技术瓶颈。
- 适合关注医疗影像智能化的科研人员与临床开发者参考。
医学影像是现代医疗的核心，推动诊断、治疗规划和患者护理的发展。在各类任务中，分割仍是最具挑战性的难题，受限于数据获取困难、标注复杂、结构变异、成像模态差异及隐私约束。尽管已有进展，实现鲁棒的泛化与域适应仍面临重大挑战，尤其是一些模型资源消耗大且依赖领域知识。本文综述医学图像分割的最新进展，重点探讨生成式AI、少样本学习、基础模型和通用模型等方法，这些技术为解决长期难题提供了新思路。系统梳理了相关理论基础、前沿技术与近期应用。最后讨论内在局限、未解问题及未来研究方向，旨在提升分割模型在医疗影像中的实用性和可及性。我们维护一个[GitHub仓库](https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation)持续追踪该领域的创新动态。
原文摘要 · Abstract (English)
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.
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