Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images
Karrar, A., Mabrouk, M., Abdel Wahed, M. (2023). Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images. EKB Journal Management System, 42(2), 1-15. doi: 10.21608/jaet.2022.83371.1117
Ayat Karrar; Mai S. Mabrouk; Manal Abdel Wahed. "Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images". EKB Journal Management System, 42, 2, 2023, 1-15. doi: 10.21608/jaet.2022.83371.1117
Karrar, A., Mabrouk, M., Abdel Wahed, M. (2023). 'Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images', EKB Journal Management System, 42(2), pp. 1-15. doi: 10.21608/jaet.2022.83371.1117
Karrar, A., Mabrouk, M., Abdel Wahed, M. Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images. EKB Journal Management System, 2023; 42(2): 1-15. doi: 10.21608/jaet.2022.83371.1117

Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images

Journal of Advanced Engineering Trends
Volume 42, Issue 2, July 2023, Page 1-15  XML PDF (1.29 MB)
Document Type: Original Article
DOI: 10.21608/jaet.2022.83371.1117
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Authors
Ayat Karrar email 1; Mai S. Mabrouk2; Manal Abdel Wahed3
1Biomedical engineering department, Cairo University, Egypt
2Misr University for science and technology
3Biomedical engineering, Faculty of engineering, Cairo University, Egypt
Abstract
Lung cancer has a high incidence rate and is considered highly fatal because of its low survival rate at early stages compared to other cancers. Computed tomography (CT) scans can reveal pulmonary nodules of different shapes and volumes in two dimensional (2D) slices. Three-dimensional (3D) reconstruction of pulmonary nodules can assist the radiologist in early treatment appropriate for the 3D nodule volume screened. In this research, we present a 3D reconstruction algorithm that uses 2D CT slices to reconstruct a 3D lung nodule. The equivalent diameters of small nodules ranged from 3 to 30 mm. A segmentation approach (based on bounding boxes and maximum intensity projection) was applied. Extracting the lung nodules from the 2D candidate masses was performed via a rule-based classifier. Surface rendering was used to reconstruct 3D pulmonary nodules which were visualized on the 3D Slicer software. The 3D nodule volume, as well as the accuracy rate and error of volume estimation were calculated. The proposed methodology was validated against the actual volumes of 14 3D nodules from the Lung Image Database Consortium (LIDC) database. The proposed algorithm achieved a maximum accuracy of 99.6627 % for lung nodule volume estimation. The corresponding average accuracy rate and average percentage error were 97.34 % and 2.66 %, respectively. The screening of 3D lung nodules can support surgery planning via nodule volume estimation. The average accuracy and error rates of the 3D reconstruction algorithm showed promising results in comparison with other published studies.
Keywords
CT scans; Surface rendering; 3D Slicer; Rule-based classification; Volume estimation
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