流匹配在医学影像三维曲线结构分割中的应用
Flow Matching Meets 3D Curvilinear Structure Segmentation in Medical Imaging
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中文总结 AI 辅助
本研究提出基于流匹配的3D-CurvSegFlow模型，用于三维医学图像的曲线结构分割，在三个不同公开数据集上的表现优于通用及血管专用方法，为医学图像分析提供高效泛化的新方向。
中文摘要 AI 辅助
在三维医学图像中对曲线状解剖结构进行分割仍面临诸多挑战，包括复杂拓扑结构、严重类别不平衡、弱对比度以及结构形态的巨大差异。尽管已有针对三维曲线分割的深度学习方法被提出，但这些方法通常针对特定解剖结构或模态定制，限制了其在不同临床场景中的泛化能力，仍有改进空间。近期生成模型已展现出迭代预测在结构化分割任务中的优势，但基于扩散的方法存在采样计算成本高昂的问题，阻碍了其在高分辨率三维体积数据上的应用。我们提出了3D-CurvSegFlow，一种基于流匹配的三维曲线结构分割模型。该模型学习从简单源分布到目标血管表示的连续变换，实现了对复杂曲线几何结构的渐进式优化，且推理效率较高。我们在三个具有挑战性的公开数据集上评估了该方法，这些数据集涵盖不同解剖结构和模态：门静脉、脑血管以及冠状动脉。在所有任务中使用相同的架构和训练策略，我们的方法在通用方法和特定血管方法中表现更优，且能很好地保留细分支和血管连续性。本研究不仅推动了三维曲线分割的最新进展，还为医学图像分析中高效、泛化且可临床应用的方法开辟了新途径。
英文摘要
Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology. While deep learning approaches for 3D curvilinear segmentation have been proposed, they are often tailored to specific anatomies or modalities, limiting generalization across clinical settings and leaving room for improvement. Recent generative models have shown the benefits of iterative prediction for structured segmentation tasks, yet diffusion-based methods suffer from computationally expensive sampling, hindering their use on high-resolution 3D volumes. We present 3D-CurvSegFlow, a flow matching-based model for 3D curvilinear structure segmentation. The model learns a continuous transformation from a simple source distribution to the target vascular representation, enabling progressive refinement of complex curvilinear geometries with efficient inference. We evaluate our method on Three public challenging datasets covering distinct anatomies and modalities: portal vein, cerebral vessel, and coronary arteries. Using a common architecture and training strategy across all tasks, our method outperforms general-purpose and vessel-specific approaches, with strong preservation of thin branches and vascular continuity. This work not only advances the state-of-the-art in 3D curvilinear segmentation but also opens new avenues for efficient, generalizable, and clinically applicable methods in medical image analysis.
发表机构
- CNRS（法国国家科学研究中心）
- Univ. Rennes（雷恩大学）
- Institute of Genetics and Development of Rennes (IGDR)（雷恩遗传与发育研究所）
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