Suppr超能文献

基于管状特征的 4D CTA 中脑血管自动中心线提取。

Automatic centerline extraction of cerebrovascular in 4D CTA based on tubular features.

机构信息

College of Computer Science and Technology, Zhejiang University of Technology, People's Republic of China.

出版信息

Phys Med Biol. 2018 Jun 19;63(12):125014. doi: 10.1088/1361-6560/aac719.

Abstract

Vascular centerlines have crucial significance in reconstruction, registration, segmentation and vascular parameter analysis. The extraction of vessel structures remains a difficult problem in the completeness and continuity of results. In this paper, we present a novel method to extract cerebrovascular centerlines from four-dimensional computed tomography angiography images. Tubular features and vascular directions are used to extract initial centerlines, and the offset correction is introduced in the vascular orthogonal plane. In addition, we also present a post-processing method to connect interruptions of centerlines. We perform a quantitative validation using clinical images and public data sets of MRA brain images. Our experimental results demonstrate that the proposed algorithm not only shows higher accuracy in complicated vessel structures, but also outperforms previous approaches in terms of high validity and universality.

摘要

血管中心线在重建、配准、分割和血管参数分析中具有至关重要的意义。血管结构的提取在结果的完整性和连续性方面仍然是一个难题。在本文中,我们提出了一种从四维计算机断层血管造影图像中提取脑血管中心线的新方法。管状特征和血管方向用于提取初始中心线,并在血管正交平面中引入偏移校正。此外,我们还提出了一种后处理方法来连接中心线的中断。我们使用临床图像和公共 MRA 脑图像数据集进行定量验证。我们的实验结果表明,所提出的算法不仅在复杂的血管结构中表现出更高的准确性,而且在有效性和通用性方面也优于以前的方法。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验