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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 convolutional neural network (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 3-D based on CNNs U-Net segmentation. It results in two types of visualization: 3-D bitmap and 3-D Mesh. 3-D 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 …