Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

Corona Virus Disease 2019 (COVID-19) has seriously threatened human life and health in just a few months. The global economy, education, transportation and other aspects have been affected. In order to solve the problems caused by COVID-19 as soon as possible, it is important to quickly and accurately confirm whether people are infected. In this paper, we take MultiResUNet as the basic model, introduce a new "Residual block" structure in the encoder part, add Regularization and Dropout to prevent training overfilling, and change the partial activation function. Propose a model suitable for COVID-19 CT image sets, which can automatically segment four parts of COVID-19 CT images (left&right lung, disease and background) by deep learning. The segmentation results are evaluated and the expected results are achieved. It is helpful for medical workers to recognize the infection area quickly.