Virtual Reality visualization for computerized COVID-19 lesion segmentation and interpretation

Oulefki, Adel; Agaian, Sos С.; Trongtirakul, Thaweesak; Benbelkacem, Samir; Aouam, Djamel; Zenati-Henda, Nadia; Abdelli, Mohamed lamine

doi:10.1016/j.bspc.2021.103371

Cited by 20 publications

(21 citation statements)

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“…Few works addressed the 3D virtual visualisation of segmented COVID-19 lung lesions to the best of our knowledge. Recently, [3] has addressed the virtual reality visualisation for computerised COVID-19 lesion segmentation. They combined CT imaging tools and VR technology to generate a system for accurately screening COVID-19 disease and navigating 3D visualisations of medical scenes to visualise dynamic 3D volumetric data.…”

Section: Related Workmentioning

confidence: 99%

“…PCR tests are widely used and considered the most accurate form available today; it takes time before the results are known CT scan images are preferred by an experienced doctor to detect the infected lesions in the lungs. CT scan benefits comprise less cost, provide valuable data, and wide availability [3] . Assessment and quantification of COVID-19 lung abnormalities based on chest Ct-scans can help determine disease stage, effectively allocate limited medical resources, and make informed treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Ct-scans used in diagnosing COVID-19 may produce false-negative effects, especially early infection; it is one of its significant weaknesses [3] . Virtual reality visualisation can remedy false-negative detection at early-stage infection [3] . The user can interact and become immersed in a computer-generated environment in a realistic way via virtual reality technology.…”

Section: Introductionmentioning

confidence: 99%

“…With VR technology, doctors can visualise the data better with more details and less work fatigue. Ct-scans used in diagnosing COVID-19 may produce false-negative effects, especially early infection; it is one of its significant weaknesses [3] . Virtual reality visualisation can remedy false-negative detection at early-stage infection [3] .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

Amara

Aouf

Kennouche

et al. 2022

Computers & Graphics

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

Amara

Aouf

Kennouche

et al. 2022

Computers & Graphics

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“…However, it is time-consuming and may provide both false-negative (FN) and false-positive (FP) rates [7]. Since COVID-19 causes lung complications, such as pneumonia, computed tomography (CT) of the scan is the most frequently used diagnostic tool [8][9][10]. In Algeria, CT scans have been widely used and show good clinical diagnostics [11].…”

Section: Introductionmentioning

confidence: 99%

COVI3D: Automatic COVID-19 CT Image-Based Classification and Visualization Platform Utilizing Virtual and Augmented Reality Technologies

et al. 2022

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Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information.

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Towards Enhancing Extended Reality for Healthcare Applications with Machine Learning

Parekh,

Oyeleke

2024

Smart Innovation, Systems and Technologies

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Virtual Reality visualization for computerized COVID-19 lesion segmentation and interpretation

Cited by 20 publications

References 50 publications

COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

COVI3D: Automatic COVID-19 CT Image-Based Classification and Visualization Platform Utilizing Virtual and Augmented Reality Technologies

Towards Enhancing Extended Reality for Healthcare Applications with Machine Learning

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