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Coronary centerline extraction based on ostium detection and model-guided directional minimal path

机译:基于孔口检测和模型指导的定向最小路径的冠状动脉中心线提取

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Extracting centerlines of coronary arteries is a challenging but important task in clinical applications of cardiac CTA. In this paper, we propose a new method to fully automatically extract the coronary centerlines. This method is based on automatic detection of ostia and the model-guided direction minimal path (DMP) framework. Both the reference positions of the ostia and the direction information are provided from a pre-constructed model. This model is first registered to the unseen CTA. Based on the registered initial positions, the two ostia of the coronary arteries are then automatically detected from the unseen CTA, using a learning-based algorithm. The detected ostia are then used as the starting points in the DMP, where the directional information of the coronary model is incorporated, to guide the path tracing procedure. This directional tracing improves the robustness of the coronary extraction. Finally, with an improved end point selection algorithm, the proposed method can extract the longest coronary centerline from a branch. We validated the proposed method by extracting the three main coronary branches.
机译:在心脏CTA的临床应用中,提取冠状动脉中心线是一项具有挑战性但重要的任务。在本文中,我们提出了一种自动提取冠状动脉中心线的新方法。此方法基于对口的自动检测和模型指导的方向最小路径(DMP)框架。孔口的参考位置和方向信息均由预先构建的模型提供。该模型首先注册到了看不见的CTA。基于注册的初始位置,然后使用基于学习的算法,从看不见的CTA中自动检测出冠状动脉的两个口。然后,将检测到的孔口用作DMP的起点,并在其中合并冠状动脉模型的方向信息,以指导路径追踪程序。这种定向跟踪提高了冠状动脉提取的坚固性。最后,通过改进的终点选择算法,该方法可以从分支中提取最长的冠状动脉中心线。我们通过提取三个主要的冠状动脉分支来验证所提出的方法。

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