3-D Visualization for Lung Covid-19 Infection Based on U-Net CNN Segmentation

Abstract

Covid-19 is still the attention of researchers in medical image analysis. Following the initial respiratory diagnosis, a CT-scan examination will be performed. Segmentation of infection within the lung area is needed as the next step after the examination. In recent years, image segmentation has been carried out with the help of Deep Learning. U-Net CNN is one of the Deep Learning-based architectures widely used in medical image segmentation. Our research aims to support radiologists in visualizing Covid-19 infection in 3D based on CNN’s U-Net segmentation. It results in two types of visualization: 3D bitmap and 3D Mesh. 3D visualization can contribute to seeing the extent of infection and calculating the predicted percentage of Covid-19 infection volume in the patient’s lungs. The dataset for training model CNN is relatively small, consisting of 20 CT scans of Zenodo’s Covid-19 patients, divided into 17 patients, 2808 images (80%) for training, and three patients, 712 images (20%) for testing. The segmentation evaluation metrics used are DICE, Precision, and Accuracy, while the evaluation metrics for 3D volume are Relative Volume Difference (RVD) and Volumetric Similarity (VS). Finally, the prediction of the percentage of infection volume to the patient’s lung volume is given. The evaluation results were satisfactory, obtaining 95% DICE scores for lung image segmentation and 75% for infection segmentation. The predicted 3D visualization volume also scored higher than 90% for both lung volume and infection. Furthermore, for calculating the prediction of infection volume against lung volume, we achieved a maximum difference of 3% from the ground truth value.