Lung Tumor Delineation Based on Novel Tumor-Background Likelihood Models in PET-CT Images

Abstract

Accurate parenchymal lung tumor delineation with PET-CT can be problematic given the inherent tumor heterogeneity and proximity / involvement of extra-parenchymal tissue. In this paper, we propose a tumor delineation approach that is based on new tumor-background likelihood models in PET and CT. By incorporating the intensity downhill feature in PET as a distance cost into the background likelihood function of CT, our delineation method avoids leakage to structures with similar intensities on PET and CT, but at the same time follows the boundary definition in CT when it is distinct. We validated our method on 40 NSCLC patient datasets with manual delineation by three clinical experts. Our method achieved an average Dice's similarity coefficient (DSC) of 0.80 ±0.08 in the simple group, and 0.77 ±0.06 in the complex group. The t-test demonstrated that our method statistically outperformed the four other methods. Our method was able to delineate complex tumors that were located in close proximity to other structures with similar intensities.