Lung infection region quantification, recognition, and virtual reality rendering of CT scan of COVID-19

Samir Benbelkacem; Adel Oulefki; Sos Agaian; Thaweesak Trongtirakul; Djamel Aouam; Nadia Zenati-Henda; Kahina Amara

doi:10.1117/12.2587757

12 April 2021 Lung infection region quantification, recognition, and virtual reality rendering of CT scan of COVID-19

Samir Benbelkacem, Adel Oulefki, Sos Agaian, Thaweesak Trongtirakul, Djamel Aouam, Nadia Zenati-Henda, Kahina Amara

Proceedings Volume 11734, Multimodal Image Exploitation and Learning 2021; 117340I (2021) https://doi.org/10.1117/12.2587757
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

In recent years, virtual reality has experienced steady growth in the medical field, such as surgery, rehabilitation, disease diagnostic, and learning. The 3D representation of radiological images plays a significant role in disease diagnostic and treatment planning compared to standard 2D medical images. Since March 2019, almost all laboratories and medical centers have improved their patients' management methods with confirmed coronavirus (COVID-19) disease. Providing appropriate treatment in the well moment may contribute to save lives. Our study aims to develop an advanced COVID-19 CT scan image segmentation and 3D visualization using an unsupervised thresholding procedure and virtual reality technology to better plan and monitor affected patients. Our proposed system provides three-dimensional COVID-19 lesion visualization, which clearly shows segmented infected region (in 3D) rather than traditional two-dimensional images.

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Samir Benbelkacem, Adel Oulefki, Sos Agaian, Thaweesak Trongtirakul, Djamel Aouam, Nadia Zenati-Henda, and Kahina Amara "Lung infection region quantification, recognition, and virtual reality rendering of CT scan of COVID-19", Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 117340I (12 April 2021); https://doi.org/10.1117/12.2587757

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