Accurate automated extraction of brain vessel centerlines from CTA images
plays an important role in diagnosis and therapy of cerebrovascular diseases,
such as stroke. However, this task remains challenging due to the complex
cerebrovascular structure, the varying imaging quality, and vessel pathology
effects. In this paper, we consider automatic lumen segmentation generation
without additional annotation effort by physicians and more effective use of
the generated lumen segmentation for improved centerline extraction
performance. We propose an automated framework for brain vessel centerline
extraction from CTA images. The framework consists of four major components:
(1) pre-processing approaches that register CTA images with a CT atlas and
divide these images into input patches, (2) lumen segmentation generation from
annotated vessel centerlines using graph cuts and robust kernel regression, (3)
a dual-branch topology-aware UNet (DTUNet) that can effectively utilize the
annotated vessel centerlines and the generated lumen segmentation through a
topology-aware loss (TAL) and its dual-branch design, and (4) post-processing
approaches that skeletonize the predicted lumen segmentation. Extensive
experiments on a multi-center dataset demonstrate that the proposed framework
outperforms state-of-the-art methods in terms of average symmetric centerline
distance (ASCD) and overlap (OV). Subgroup analyses further suggest that the
proposed framework holds promise in clinical applications for stroke treatment.
Code is publicly available at this https URL

提出了一个自动化的框架来从 CTA 图像中提取脑血管中心线，该框架包括预处理方法、基于图割和稳健核回归的管腔分割、采用拓扑感知损失和双分支设计的双分支拓扑感知 UNet (DTUNet)，以及骨架化预测的管腔分割的后处理方法，实验证明该框架在平均对称中心线距离 (ASCD) 和重叠度 (OV) 方面优于现有方法，并且子组分析进一步表明该框架在中风治疗的临床应用中有潜力。