Abstract
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 this https URL
Abstract (translated)
过去二十年来，医学图像分割领域取得了快速进展，这主要得益于深度学习技术的应用，它使得细胞、组织、器官及各种病理在不同成像模式下的准确高效边界划分成为可能。这一进步引发了一个基本问题：目前的模型在多大程度上克服了持久存在的挑战？还有哪些差距需要填补？在这项工作中，我们对医学图像分割进行了深入回顾，追踪了过去十年中的进展和关键发展。我们考察了核心原则，包括多尺度分析、注意力机制以及先验知识集成，在分割网络的编码器、瓶颈层、跳跃连接和解码器组件中均有体现。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) 从器官分割向病灶任务的关注变化；(3) 多模态集成与领域适应的进步；(4) 基础模型和迁移学习的作用；(5) 从确定性划分向概率划分的演进；(6) 从二维到三维及四维分割的发展；以及 (7) 从调用模型向分割代理的趋势。这些视角共同提供了一个基于深度学习医学图像分割的整体概览，并旨在激发未来的创新。为了支持持续的研究，我们维护了一份不断更新的相关文献和开源资源库，可在此处的[URL]访问。 请注意，在原文中提到的具体网址（this https URL）未给出实际链接地址，请根据上下文或使用原始文档来查找该链接的实际位置。
URL
https://arxiv.org/abs/2508.20139
