Tumor Segmentation on 18F FDG-PET Images Using Graph Cut and Local Spatial Information

The purpose of this study was to develop methodology to segment tumors on 18F-fluorodeoxyg-lucose (FDG) positron emission tomography (PET) images. Sixty-four metastatic bone tumors were included. Graph cut was used for tumor segmentation, with segmentation energy divided in-to unary and pairwise terms. Locally connected conditional random fields (LCRF) were proposed for the pairwise term. In LCRF, three-dimensional cubic window with length L was set for each voxel, and voxels within the window were considered for the pairwise term. Three other types of segmentation were applied: region-growing based on 35%, 40%, and 45 % of the tumor maximum standardized uptake value (RG35, RG40, and RG45, respectively), SLIC superpixels (SS), and re-gion-based active contour models (AC). To validate the tumor segmentation accuracy, dice simi-larity coefficients (DSC) were calculated between the result of each technique and manual seg-mentation. Differences in DSC were tested using the Wilcoxon signed-rank test. Mean DSCs fo