Thermal damage map prediction during irreversible electroporation with U-Net

Abstract

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.