Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors

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Abstract

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.

Figures

  • Fig 1. Two different electrode configurations and two different positions of the vessel. A: 4 electrodes with vessel perpendicular to them. B: 5 electrodes with vessel parallel to them. “d” denotes distance from vessel to tumor.
  • Table 1. Electrode number and position per tumor size and treatment type.
  • Table 2. Values of electroporation thresholds and conductivities for different tissues with corresponding sources.
  • Fig 2. Real patient cases used in our study. A: MRI of the liver for first patient case. B: Reconstructed 3D model of the first case with inserted electrodes. C: MRI of the liver for second patient case. D: Reconstructed 3D model of the second case with inserted electrodes. In all images the structure colored green is the tumor, while the nearby major vessels are colored blue.
  • Table 3. Optimal voltages for a model without the vessel, per tumor size, treatment type and electrode number.
  • Fig 3. Tumor coverage for ECT of simplified model of 10 mm tumor with 4 electrodes. The coverage is plotted against different distances between vessel and tumor, and with respect to different vessel positions and sizes. A: Vessel perpendicular to the electrodes. B: Vessel parallel to the electrodes.
  • Fig 4. Tumor coverage for ECT of simplified model of 10 mm tumor with 5 electrodes. The coverage is plotted against different distances between vessel and tumor, and with respect to different vessel positions and sizes. A: Vessel perpendicular to the electrodes. B: Vessel parallel to the electrodes.
  • Fig 5. Tumor coverage for ECT of simplified model of 30 mm tumor. The coverage is plotted against different distances between vessel and tumor, and with respect to different vessel positions and sizes. A: Vessel perpendicular to the electrodes. B: Vessel parallel to the electrodes.

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CITATION STYLE

APA

Marčan, M., Kos, B., & Miklavčič, D. (2015). Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors. PLoS ONE, 10(5). https://doi.org/10.1371/journal.pone.0125591

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