Virtual Reality visualization for computerized COVID-19 lesion segmentation and interpretation. (March 2022)

• Record Type: Journal Article
• Authors: Oulefki, Adel; Agaian, Sos; Trongtirakul, Thaweesak; Benbelkacem, Samir; Aouam, Djamel; Zenati-Henda, Nadia; Abdelli, Mohamed-Lamine
• Is Part Of: Biomedical signal processing and control. Volume 73(2022)
• Publication Date: 2022-03
• Subjects: COrona-VIrus Disease (COVID-19) -- COVID lesion segmentation -- VR visualization -- Lesion measurements; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103371
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Coronavirus disease (COVID-19) is a severe infectious disease that causes respiratory illness and has had devastating medical and economic consequences globally. Therefore, early, and precise diagnosis is critical to control disease progression and management. Compared to the very popular RT-PCR (reverse-transcription polymerase chain reaction) method, chest CT imaging is a more consistent, sensible, and fast approach for identifying and managing infected COVID-19 patients, specifically in the epidemic area. CT images use computational methods to combine 2D X-ray images and transform them into 3D images. One major drawback of CT scans in diagnosing COVID-19 is creating false-negative effects, especially early infection. This article aims to combine novel CT imaging tools and Virtual Reality (VR) technology and generate an automatize system for accurately screening COVID-19 disease and navigating 3D visualizations of medical scenes. The key benefits of this system are a) it offers stereoscopic depth perception, b) give better insights and comprehension into the overall imaging data, c) it allows doctors to visualize the 3D models, manipulate them, study the inside 3D data, and do several kinds of measurements, and finally d) it has the capacity of real-time interactivity and accurately visualizes dynamic 3D volumetric data. The tool provides novel visualizations for medical practitioners to identify and analyze the change in the shape of COVID-19 infectious. The second objective of this work is to generate, the first time, the CT African patient COVID-19 scan datasets containing 224 patients positive for an infection and 70 regular patients CT-scan images. Computer simulations demonstrate that the proposed method's effectiveness comparing with state-of-the-art baselines methods. The results have also been evaluated with medical professionals. The developed system could be used for medical education professional training and a telehealth VR platform.