Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification

• Published in: Healthcare (Basel) Vol. 11; no. 2; p. 213
• Main Authors: Asnawi, Mohammad Hamid, Pravitasari, Anindya Apriliyanti, Darmawan, Gumgum, Hendrawati, Triyani, Yulita, Intan Nurma, Suprijadi, Jadi, Nugraha, Farid Azhar Lutfi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.01.2023; MDPI
• Subjects: 3D DenseUNet; 3D image segmentation; 3D ResUNet; 3D UNet; 3D VGGUNet; Accuracy; Algorithms; Artificial intelligence; Automation; Bacterial infections; Classification; Coronaviruses; COVID-19; COVID-19 CT-scan; Deep learning; Infections; Lung diseases; Machine learning; Medical diagnosis; Medical personnel; Medical research; Pandemics; Radiography; Respiratory diseases; Semantics; Severe acute respiratory syndrome coronavirus 2; 3D ResUNet; 3D image segmentation; 3D VGGUNet; 3D UNet; COVID-19 CT-scan; 3D DenseUNet
• DOI: 10.3390/healthcare11020213
• ISSN: 2227-9032

COVID-19 is the disease that has spread over the world since December 2019. This disease has a negative impact on individuals, governments, and even the global economy, which has caused the WHO to declare COVID-19 as a PHEIC (Public Health Emergency of International Concern). Until now, there has been no medicine that can completely cure COVID-19. Therefore, to prevent the spread and reduce the negative impact of COVID-19, an accurate and fast test is needed. The use of chest radiography imaging technology, such as CXR and CT-scan, plays a significant role in the diagnosis of COVID-19. In this study, CT-scan segmentation will be carried out using the 3D version of the most recommended segmentation algorithm for bio-medical images, namely 3D UNet, and three other architectures from the 3D UNet modifications, namely 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. These four architectures will be used in two cases of segmentation: binary-class segmentation, where each architecture will segment the lung area from a CT scan; and multi-class segmentation, where each architecture will segment the lung and infection area from a CT scan. Before entering the model, the dataset is preprocessed first by applying a minmax scaler to scale the pixel value to a range of zero to one, and the CLAHE method is also applied to eliminate intensity in homogeneity and noise from the data. Of the four models tested in this study, surprisingly, the original 3D UNet produced the most satisfactory results compared to the other three architectures, although it requires more iterations to obtain the maximum results. For the binary-class segmentation case, 3D UNet produced IoU scores, Dice scores, and accuracy of 94.32%, 97.05%, and 99.37%, respectively. For the case of multi-class segmentation, 3D UNet produced IoU scores, Dice scores, and accuracy of 81.58%, 88.61%, and 98.78%, respectively. The use of 3D segmentation architecture will be very helpful for medical personnel because, apart from helping the process of diagnosing someone with COVID-19, they can also find out the severity of the disease through 3D infection projections.