标题： 医学图像分割问题是否已解决？ 当前发展与未来方向的综述 显示英文标题

标题： Is the medical image segmentation problem solved? A survey of current developments and future directions

摘要： 医学图像分割在过去二十年中迅速发展，主要由深度学习推动，这使得在多种成像模式下准确且高效地勾画细胞、组织、器官和病灶成为可能。 这一进展引发了一个基本问题：当前模型在多大程度上克服了持续的挑战，还存在哪些差距？ 在本工作中，我们对医学图像分割进行了深入综述，追溯了过去十年的进展和关键发展。 我们考察了分割网络中编码器、瓶颈层、跳跃连接和解码器组件的核心原理，包括多尺度分析、注意力机制以及先验知识的整合。 我们的讨论围绕七个关键维度展开：(1) 从监督学习到半监督/无监督学习的转变， (2) 从器官分割到病灶聚焦任务的转变，(3) 多模态整合和领域适应的进步，(4) 基础模型和迁移学习的作用，(5) 从确定性分割到概率分割的转变，(6) 从2D到3D和4D分割的发展，以及(7) 从模型调用到分割代理的趋势。 这些视角共同提供了基于深度学习的医学图像分割发展轨迹的全面概述，并旨在激发未来的创新。 为了支持持续的研究，我们在 https://github.com/apple1986/medicalSegReview 维护一个不断更新的相关文献和开源资源库。 显示英文摘要

摘要： Medical image segmentation has advanced rapidly over the past two decades, largely driven by deep learning, which has enabled accurate and efficient delineation of cells, tissues, organs, and pathologies across diverse imaging modalities. This progress raises a fundamental question: to what extent have current models overcome persistent challenges, and what gaps remain? In this work, we provide an in-depth review of medical image segmentation, tracing its progress and key developments over the past decade. We examine core principles, including multiscale analysis, attention mechanisms, and the integration of prior knowledge, across the encoder, bottleneck, skip connections, and decoder components of segmentation networks. Our discussion is organized around seven key dimensions: (1) the shift from supervised to semi-/unsupervised learning, (2) the transition from organ segmentation to lesion-focused tasks, (3) advances in multi-modality integration and domain adaptation, (4) the role of foundation models and transfer learning, (5) the move from deterministic to probabilistic segmentation, (6) the progression from 2D to 3D and 4D segmentation, and (7) the trend from model invocation to segmentation agents. Together, these perspectives provide a holistic overview of the trajectory of deep learning-based medical image segmentation and aim to inspire future innovation. To support ongoing research, we maintain a continually updated repository of relevant literature and open-source resources at https://github.com/apple1986/medicalSegReview