Augmented Reality for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning
Kahina Amara, Ali Aouf, Oussama Kerdjidj, Hoceine Kennouche, Oualid Djekoune, Mohamed Amine Guerroudji, Nadia Zenati, Djamel Aouam
Abstract
The virus new variants of Coronavirus disease 2019 (COVID-19) continue to appear, making the situation more challenging and threatening. The COVID-19 pandemic has profoundly affected health systems and medical centres worldwide. The primary clinical tools used in diagnosing patients presenting with respiratory distress and suspected COVID-19 symptoms are radiology examinations. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical specialists. This paper presents an Augmented Reality (AR) tool for COVID-19 aid diagnosis, including Computerised Tomography Ct-scans segmentation based Deep Learning, 3D reconstruction, and AR visualisation. Segmentation is a critical step in AI-based COVID-19 image processing and analysis; we use the popular segmentation networks, including classic U-Net. Quantitative and qualitative evaluation showed reasonable performance of U-Net for lung and COVID-19 lesions segmentation. The AR-COVID-19 aid diagnosis system could be used for medical education professional training and as a support visualisation and reading tool for radiologist.