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Effect of Blood Vessel Segmentation on the Outcome of Electroporation-Based Treatments of Liver Tumors

机译:血管分割对基于电穿孔的肝肿瘤治疗结果的影响

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摘要

Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm) and medium-sized (30 mm) tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.
机译:基于电穿孔的治疗依赖于通过电极施加在组织上的高压电脉冲来增加细胞膜的通透性。为了确保整个肿瘤都被足够高的电场覆盖,会根据患者的个人解剖结构建立精确的数值模型。通过分割医学图像来提取患者的解剖结构不可避免地会产生一些错误。为了确保治疗计划的鲁棒性,有必要评估此类误差对电场分布的潜在影响。在这项工作中,我们专注于确定基于肝细胞电穿孔治疗的肝血管自动分割错误对电场分布的影响。首先,在简单的“球形和圆柱”模型上对不同尺寸和相对位置的肿瘤和血管进行了数值分析。其次,分析了从真实患者的医学图像中提取的两个模型,在其中我们介绍了自动血管分割方法的误差变化。从简单模型获得的结果表明,在计算电场分布时忽略血管可能会导致电场对肿瘤的覆盖不足。这项研究的结果表明,这种效应发生在小(10毫米)和中等大小(30毫米)的肿瘤上,特别是在没有在肿瘤中插入中央电极的情况下。从实际模型中获得的结果还显示,当缺少中央电极时,自动血管分割错误对电场分布的负面影响更大。但是,如果存在中央电极,则自动血管分割的平均误差不会对电场分布产生影响。这表明该算法足够健壮,可用于创建用于治疗参数优化的模型,但带有中心电极。

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