A Method to Automate the Segmentation of the GTV and ITV for Lung Tumors

Abstract 

Four-dimensional computed tomography (4D-CT) is a useful tool in the treatment of tumors that undergo significant motion. To fully utilize 4D-CT motion information in the treatment of mobile tumors such as lung cancer, autosegmentation methods will need to be developed. Using autosegmentation tools in the Pinnacle³ v8.1t treatment planning system, 6 anonymized 4D-CT data sets were contoured. Two test indices were developed that can be used to evaluate which autosegmentation tools to apply to a given gross tumor volume (GTV) region of interest (ROI). The 4D-CT data sets had various phase binning error levels ranging from 3% to 29%. The appropriate autosegmentation method (rigid translational image registration and deformable surface mesh) was determined to properly delineate the GTV in all of the 4D-CT phases for the 4D-CT data sets with binning errors of up to 15%. The ITV was defined by 2 methods: a mask of the GTV in all 4D-CT phases and the maximum intensity projection. The differences in centroid position and volume were compared with manual segmentation studies in literature. The indices developed in this study, along with the autosegmentation tools in the treatment planning system, were able to automatically segment the GTV in the four 4D-CTs with phase binning errors of up to 15%.

Key Words: Automated segmentation, 4D treatment planning, Phase binning error, Maximum intensity projection