Conductivity change with needle electrode during high frequency irreversible electroporation: a finite element study

Thermal damage map prediction during irreversible electroporation with U-Net

Recent developments in cancer treatment with irreversible electroporation (IRE) have led to a renewed interest in developing a treatment planning system based on Deep-Learning methods. This paper will give an account of U-Net, as a Deep-Learning architecture usage for predicting thermal damage area during IRE. In this study, an irregular shape of the liver tumor with MIMICS and 3-Matic software was created from Magnetic Resonance Imaging (MRI) images. To create electric field distribution and thermal damage maps in IRE, COMSOL Multiphysics 5.3 finite element analysis was performed. It was decided to use the pair needle, single bipolar, and multi-tine electrodes with different geometrical parameters as electrodes. The U-Net was designed as a Deep-Learning network to train and predict the thermal damage area from electric field distribution in the IRE. The average DICE coefficient and accuracy of trained U-Net for predicting thermal damage area on test data sets were 0.96 and 0.98, respectively, for the dataset consisting of all electrode type electric field intensity images. This is the first time that U-Net has been used to predict thermal damage area. The results of this research support the idea that the U-Net can be used for predicting thermal damage areas during IRE as a treatment planning system.

Automated irreversible electroporated region prediction using deep neural network, a preliminary study for treatment planning

The primary purpose of cancer treatment with irreversible electroporation (IRE) is to maximize tumor damage and minimize surrounding healthy tissue damage. Finite element analysis is one of the popular ways to calculate electric field and cell kill probability in IRE. However, this method also has limitations. This paper will focus on using a deep neural network (DNN) in IRE to predict irreversible electroporated regions for treatment planning purposes. COMSOL Multiphysics was used to simulate the IRE. The electric conductivity change during IRE was considered to create accurate data sets of electric field distribution and cell kill probability distributions. We used eight pulses with a pulse width of 100 μs, frequency of 1 Hz, and different voltages. To create masks for DNN training, a 90% cell kill probability contour was used. After data set creation, U-Net architecture was trained to predict irreversible electroporated regions. In this study, the average U-Net DICE coefficient on test data was 0.96. Also, the average accuracy of U-Net for predicting irreversible electroporated regions was 0.97. As far as we are aware, this is the first time that U-Net was used to predict an irreversible electroporated region in IRE. The present study provides significant evidence for U-Net's use for predicting an irreversible electroporated region in treatment planning.