Ground-glass nodule segmentation in chest CT images using asymmetric multi-phase deformable model and pulmonary vessel removal

Abstract

We propose a ground-glass nodule (GGN) segmentation method that can separate solid component and ground-glass opacity (GGO) using an asymmetric multi-phase deformable model in chest CT images. First, initial solid component and GGO were extracted using intensity-based segmentation with histogram modeling. Second, the initial extracted regions were refined using an asymmetric multi-phase deformable model with modified energy functional and intensity-constrained averaging function. Finally, vessel-like structures are removed based on multi-scale shape analysis. In experiments, the segmentation accuracy of the entire GGN was evaluated using datasets from SNUH and LIDC/IDRI. The average DSC values of Seoul National University Hospital (SNUH) and Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) were 0.85 ± 0.05 and 0.78 ± 0.07, respectively. The Pearson's correlation coefficient (r) between segmented volumes by the proposed method and manual segmentation was evaluated using SNUH dataset. The r of solid component, GGO, and entire GGN were 0.931, 0.875 and 0.907. Our experimental results show that the proposed method improves segmentation accuracy by applying the proposed asymmetric multiphase deformable model and pulmonary vessel removal.