Two-Dimensional Segmentation to Reconstruct Three-Dimensional Covid-19 Patient’s Lung CT Using Active Contour

Zaki Ambadar (Biomedical Engineering, Institut Teknologi Sepuluh Nopember)
Tri Arief Sardjono (Institut Teknologi Sepuluh Nopember Surabaya (ITS))
Nada Fitrieyatul Hikmah (Biomedical Engineering, Institut Teknologi Sepuluh Nopember)

International Journal of Computer Science and Humanitarian AI
Vol. 2 No. 1 (2025): IJCSHAI

Beginning in December 2019, SArS-CoV-2, also referred to as COVID-19, quickly spread over the world. With two recurrent waves and a 3.3% fatality rate, COVID-19 has caused over 4 million cases in Indonesia. RT-PCR, antigen, and RT-LAMP are currently the main techniques for COVID-19 detection and diagnosis. A CT scan is usually used for additional diagnosis when RT-PCR results are uncertain, but extra confirmation is required. The need to inform patients about the effects of COVID-19 on the lungs is increasing as the number of cases of the virus keeps rising and diagnosis and first aid techniques advance. The severity of COVID-19-induced pneumonia, which shows up as ground-glass opacities (GGO), which are gray patches in the lung cavity, may be seen on a single-slice CT scan. The degree of lung injury can be measured using image processing techniques. In this study, two- and three-dimensional representations of the lungs were created utilizing a multi-slice CT scan and image processing techniques like active contour and marching cubes. The suggested approach produced an average volume difference of 5% and an accuracy of 62% based on intersection over union (IoU).