Segmentation Refinement of Small-Size Juxta-Pleural Lung Nodules in CT Scans

Abstract

Background: In order to evaluate the growth rate of lung cancer, pulmonary nodule segmentation is an essential and crucial step. Segmentation of juxta-pleural pulmonary nodule in CT scans, especially small size ones, is still a challenge. Objectives: To better support the following radiomics analysis, this study aims to propose and develop a novel segmentation method for small-size juxta-pleural pulmonary nodules. Materials and Methods: In this study, we investigated and developed a novel approach based on transition region thresholding and chain code analysis to segment juxta-pleural pulmonary nodules. First, we cropped the region of interest (ROI) from the lung CT scans, and enhanced the nodule regions by using an anisotropic diffusion algorithm. Second, to extract the foreground pixels (including the attached chest wall) from ROIs, we applied an adaptive segmentation process by incorporating a threshold segmentation method with transition region analysis. Third, we smoothed the lung contour by using iterative weighted averaging algorithm. Then, we utilized chain code analysis to repair lung parenchyma boundaries. Finally, we obtained the segmentation result by overlapping the extracted foreground with the repaired lung parenchyma mask. Results: To validate the performance of the proposed segmentation approach, we selected 50 juxta-pleural nodules with diameter ranges from 5 mm to 10 mm from Lung Image Database Consortium (LIDC) database. Compared with the ground truth generated by radiologists, we achieved an average overlap rate of 76.93% ± 0.06 with a false positive rate of 13.09% ± 0.09. Conclusion: After comparing and analyzing the segmentation results, we found that our approach outperformed the method reported in other literature. The experimental results demonstrated that our new method is an effective approach to segment small-size juxta-pleural pulmonary nodules accurately.