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血管结构弯曲的自动地标定位:应用于颈内动脉的对比研究。

Automated landmarking of bends in vascular structures: a comparative study with application to the internal carotid artery.

机构信息

Department of Computational Physiology, Simula Research Laboratory AS, Kristian Augusts gate 23, 0164, Oslo, Norway.

出版信息

Biomed Eng Online. 2021 Nov 27;20(1):120. doi: 10.1186/s12938-021-00957-6.

Abstract

Automated tools for landmarking the internal carotid artery (ICA) bends have the potential for efficient and objective medical image-based morphometric analysis. The two existing algorithms rely on numerical approximations of curvature and torsion of the centerline. However, input parameters, original source code, comparability, and robustness of the algorithms remain unknown. To address the former two, we have re-implemented the algorithms, followed by sensitivity analyses. Of the input parameters, the centerline smoothing had the least impact resulting in 6-7 bends, which is anatomically realistic. In contrast, centerline resolution showed to completely over- and underestimated the number of bends varying from 3 to 33. Applying the algorithms to the same cohort revealed a variability that makes comparison of results between previous studies questionable. Assessment of robustness revealed how one algorithm is vulnerable to model smoothness and noise, but conceptually independent of application. In contrast, the other algorithm is robust and consistent, but with limited general applicability. In conclusion, both algorithms are equally valid albeit they produce vastly different results. We have provided a well-documented open-source implementation of the algorithms. Finally, we have successfully performed this study on the ICA, but application to other vascular regions should be performed with caution.

摘要

用于定位颈内动脉 (ICA) 弯曲的自动工具具有进行高效和客观的基于医学图像的形态计量分析的潜力。现有的两种算法依赖于中心线曲率和扭转的数值逼近。然而,算法的输入参数、原始源代码、可比性和稳健性仍然未知。为了解决前两个问题,我们重新实现了这些算法,并进行了敏感性分析。在输入参数中,中心线平滑的影响最小,导致出现 6-7 个弯曲,这在解剖学上是现实的。相比之下,中心线分辨率显示出完全高估和低估弯曲数量的情况,从 3 到 33 不等。将这些算法应用于同一队列中,发现了一种可变性,使得对先前研究结果进行比较变得值得怀疑。稳健性评估表明,一种算法容易受到模型平滑度和噪声的影响,但在概念上独立于应用。相比之下,另一种算法是稳健且一致的,但通用性有限。总之,这两种算法都是有效的,尽管它们产生了截然不同的结果。我们已经提供了算法的经过充分记录的开源实现。最后,我们已经成功地在 ICA 上执行了此研究,但应谨慎将其应用于其他血管区域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8439/8626959/3f373214b71f/12938_2021_957_Fig1_HTML.jpg

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