用扩散模型更自然地提取3D CT血管中心线
VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images
- 采用粗到精的中心线表示与投票聚合机制
- 在公开数据集上准确率优于传统方法
- 适合医学图像分析与血管建模研究者
从3D CT图像中提取血管中心线是一项重要任务，可减少构建血管结构模型所需的标注工作量。由于传统方法为确定性模型，难以捕捉复杂的人体血管结构，因此存在挑战。本文提出VesselFusion，一种基于扩散模型的3D CT图像血管中心线提取方法。该方法采用粗到精的中心线表示策略，并引入基于投票的聚合机制，实现更自然、稳定的中心线提取。在公开可用的CT图像数据集上评估显示，VesselFusion在提取精度和结果自然度方面均优于传统方法。
原文摘要 · Abstract (English)
Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to estimate natural vessel structures since conventional approaches are deterministic models, which cannot capture a complex human structure. In this study, we propose VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image. The proposed method uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction. VesselFusion was evaluated on a publicly available CT image dataset and achieved higher extraction accuracy and a more natural result than conventional approaches.
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