用于计算动脉瘤几何形状的GUI,可能是预测血管破裂风险的工具

M. Ilea, M. Rotariu, A. Gheorghiță
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引用次数: 0

摘要

动脉瘤的几何形状是分析和预测破裂风险的重要因素,也是选择适合手术干预的患者的重要因素,例如血管内Guglielmi可拆卸线圈术。评估腹主动脉瘤(AAA)破裂风险最常用的技术是主动脉的最大直径(Dmax),但也有其他尺寸用于评估脑动脉瘤的风险。在大多数情况下,动脉瘤的几何形状是通过计算机断层扫描(CT)或三维血管造影(3D)人工测量的。通过这种方法,在重复测量期间,准确度和再现性可以因用户而异,甚至在同一用户的情况下也是如此。我们提出了基于GUI的软件,用于自动和半自动地处理包含动脉瘤的2D图像,可用于虚拟教育或电子学习过程。首先对图像进行分割,用户可以选择点,使用计算几何算法自动提取几何参数。如果用户选择此选项,则会对描述再现性形状的各种参数进行统计分析。颅内动脉瘤几何量化软件有一个单独的模块用于预测破裂的风险。一个专门的模块用于处理原始图像;这种情况下的操作是基于用户的交互式选择:点和多边形,并可选择提取不同的形状描述符进行特征选择。该阶段的预测可以从与一组数据相关的两个选项中手动选择:线性预测和基于简单回归非线性模型的非线性预测。结果非常令人鼓舞,并考虑到未来的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A GUI FOR COMPUTING ANEURYSM GEOMETRY, A POSSIBLE TOOL TO PREDICT THE RISK OF RUPTURE OF BLOOD VESSELS
The aneurysm geometry is an important factor in analyze and predict the risk of rupture but also in selection of patients suitable for surgical intervention, e.g. endovascular Guglielmi detachable coiling. The most used actually technique for evaluation the risk for abdominal aortic aneurysm (AAA) rupture is the maximum diameter (Dmax) of the aorta, but also other dimensions are used for assessment of risk for cerebral aneurysm. In most of the cases, the aneurysm geometry is measured manually using computed tomography (CT) or three-dimensional (3D) angiography. By this method, the accuracy and reproducibility can vary substantially from user to user and even in the case of the same user, during the repeatedly measurements. We propose software based on GUI for automatic and semi-automatic usage for processing of 2D images, containing aneurysm, useful in virtual education or e-learning process. The image is first segmented, and the user can select points to perform automatically extract of geometrical parameters using algorithms from computational geometry. The various parameters that describe the shape of reproducibility are statistically analyzed if the user selects this option. The software for intracranial aneurysm geometry quantification has a separate module used for prediction the risk of rupture. A specialized module is used for work with original image; the operations in this case are based on interactive selections made by user: points and polygons with option to extract different shape descriptor for feature selection. The prediction in this stage can be manually selected from two options related to a set of data: a liner one and a nonlinear one based of simple regressive nonlinear model. The results are very encouraging and the future developments are taken into account.
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