Automated volumetric segmentation method for growth consistency of nonsolid pulmonary nodules in high-resolution CT

There is widespread clinical interest in the study of pulmonary nodules for early diagnosis of lung cancer. These nodules can be broadly classified into one of three types, solid, nonsolid and part-solid. Solid nodules have been extensively studied, while little research has focused on the characterization of nonsolid and part-solid nodules. Nonsolid nodules have an appearance in high-resolution CT consisting of voxels only slightly more dense than that of the surrounding lung parenchyma. For the solid nodule, robust techniques are available to estimate growth rate and this is commonly used to distinguish benign from malignant. For the nonsolid types, these techniques are less well developed. In this research, we propose an automated volumetric segmentation method for nonsolid nodules that accurately determines a nonsolid nodules growth rate. Our method starts with an initial noise-filtering stage in the parenchyma region. Each voxel is then classified into one of three tissue types; lung parenchyma, nonsolid and solid. Removal of vessel attachments to the lesion is achieved with the use of a filter that focuses on vessel characteristics. Our results indicate that the automated method is more consistent than the radiologist with a median growth consistency of 1.87 compared to 3.12 for the radiologist on a database of 25 cases.