用可变形中心线连续表示血管，解决分割碎片化问题。
DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image
- 提出可变形中心线连续表示，以节点与边建模血管空间关系。
- 在4个3D血管数据集上分割精度显著提升，优于传统离散方法。
- 适合医学影像中复杂曲血管的精确提取，临床可视化效果佳。
在3D医学成像领域，准确提取和表示具有曲线结构的血管对临床诊断至关重要。以往方法常依赖离散的掩码表示，因像素级分类范式的局限性，易产生局部断裂或零散片段。本文提出DeformCL，一种基于可变形中心线的新型连续表示，其中中心线点作为节点，通过边捕捉空间关联。相比传统表示，DeformCL具备自然连通性、抗噪鲁棒性和交互便捷性。我们设计了分层级联的训练流程，充分挖掘其优势。在4个3D血管分割数据集上的大量实验验证了方法的有效性与优越性。此外，曲面重建图像的可视化进一步证实了该框架的临床价值。代码已开源：https://github.com/barry664/DeformCL。
原文摘要 · Abstract (English)
In the field of 3D medical imaging, accurately extracting and representing the blood vessels with curvilinear structures holds paramount importance for clinical diagnosis. Previous methods have commonly relied on discrete representation like mask, often resulting in local fractures or scattered fragments due to the inherent limitations of the per-pixel classification paradigm. In this work, we introduce DeformCL, a new continuous representation based on Deformable Centerlines, where centerline points act as nodes connected by edges that capture spatial relationships. Compared with previous representations, DeformCL offers three key advantages: natural connectivity, noise robustness, and interaction facility. We present a comprehensive training pipeline structured in a cascaded manner to fully exploit these favorable properties of DeformCL. Extensive experiments on four 3D vessel segmentation datasets demonstrate the effectiveness and superiority of our method. Furthermore, the visualization of curved planar reformation images validates the clinical significance of the proposed framework. We release the code in https://github.com/barry664/DeformCL
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