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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: particle filtering.
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Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: particle filtering.

机译：通过顺序蒙特卡洛方法对脑动脉段进行稳健的分割：粒子滤波。

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摘要

In this paper a method to extract cerebral arterial segments from CT angiography (CTA) is proposed. The segmentation of cerebral arteries in CTA is a challenging task mainly due to bone contact and vein contamination. The proposed method considers a vessel segment as an ellipse travelling in three-dimensional (3D) space and segments it out by tracking the ellipse in spatial sequence. A particle filter is employed as the main framework for tracking and is equipped with adaptive properties to both bone contact and vein contamination. The proposed tracking method is evaluated by the experiments on both synthetic and actual data. A variety of vessels were synthesized to assess the sensitivity to the axis curvature change, obscure boundaries, and noise. The experimental results showed that the proposed method is also insensitive to parameter settings and requires less user intervention than the conventional vessel tracking methods, which proves its improved robustness.
机译：本文提出了一种从CT血管造影（CTA）提取脑动脉节段的方法。 CTA中的脑动脉分割是一项艰巨的任务，主要是由于骨骼接触和静脉污染。所提出的方法将血管段视为在三维（3D）空间中移动的椭圆，并通过按空间顺序跟踪椭圆来将其分段。粒子过滤器被用作跟踪的主要框架，并具有针对骨骼接触和静脉污染的自适应属性。通过对合成数据和实际数据的实验对提出的跟踪方法进行了评估。合成了多种容器以评估对轴曲率变化，模糊边界和噪声的敏感性。实验结果表明，与传统的血管跟踪方法相比，该方法对参数设置不敏感，需要较少的用户干预，证明了其鲁棒性。

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