Deeply Supervised U-Net with Feature Fusion: Automatic COVID-19 Lung Infection Segmentation from CT Images

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. In this work, we present a deep learning based framework for automatic segmentation of pathologic COVID-19 associated tissue areas from clinical CT images available from a dataset with 108 cases in China. More specifically, we present an effective multi-scale feature fusion U-Net equipped with ResNet architecture and a deep supervision mechanism to increase the network's capacity for learning richer representations of infected tissue. Our experiments demonstrate that our model achieves an average dice score (0.674), sensitivity (0.733) and Precision (0.714) on the dataset. The experimental results have indicated the effectiveness of the proposed improvements and the potential of our proposed method for real clinical practice.