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Coronary artery segmentation using geometric moments based tracking and snake-driven refinement

机译:使用基于几何矩的跟踪和蛇驱动精细化技术进行冠状动脉分割

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Automatic or semi-automatic segmentation and tracking of artery trees from computed tomography angiography (CTA) is an important step to improve the diagnosis and treatment of artery diseases, but it still remains a significant challenging problem. In this paper, we present an artery extraction method to address the challenge. The proposed method consists of two steps: (1) a geometric moments based tracking to secure a rough centerline, and (2) a fully automatic generalized cylinder structure-based snake method to refine the centerlines and estimate the radii of the arteries. In this method, a new line direction based on first and second order geometric moments is adopted while both gradient and intensity information are used in the snake model to improve the accuracy. The approach has been evaluated on synthetic images as well as 8 clinical coronary CTA images with 32 coronary arteries. Our method achieves 94.7% overlap tracking ability within an average distance inside the vessel of 0.36mm.
机译:通过计算机断层扫描血管造影(CTA)对动脉树进行自动或半自动分割和跟踪是改善动脉疾病的诊断和治疗的重要步骤,但仍然是一个重大的挑战性问题。在本文中,我们提出了一种动脉提取方法来应对这一挑战。所提出的方法包括两个步骤:(1)基于几何矩的跟踪以确保粗略的中心线,以及(2)全自动的基于广义圆柱结构的基于蛇形的方法来细化中心线并估计动脉半径。在该方法中,采用基于一阶和二阶几何矩的新线方向,同时在蛇形模型中同时使用了梯度和强度信息以提高精度。该方法已经在合成图像以及具有32个冠状动脉的8个临床冠状动脉CTA图像上进行了评估。我们的方法在0.36mm的血管内平均距离内实现了94.7%的重叠跟踪能力。

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