Author(s)

Mizuho Nishio^1,2*, Atsushi K. Kono³, Kazuhiro Kubo⁴, Hisanobu Koyama³, Tatsuya Nishii³, Kazuro Sugimura³

¹Clinical PET Center, Institute of Biomedical Research and Innovation, Kobe, Japan.
²Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
³Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
⁴Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Japan.

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 into 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 region-based active contour models (AC). To validate the tumor segmentation accuracy, dice similarity coefficients (DSC) were calculated between the result of each technique and manual segmentation. Differences in DSC were tested using the Wilcoxon signed-rank test. Mean DSCs for LCRF at L = 3, 5, 7, and 9 were 0.784, 0.801, 0.809, and 0.812, respectively. Mean DSCs for the other techniques were: RG35, 0.633; RG40, 0.675; RG45, 0.689; SS, 0.709; and AC, 0.758. The DSC differences between LCRF and other techniques were statistically significant (p < 0.05). Tumor segmentation was reliably performed with LCRF.

Image Segmentation, FDG-PET, Graph Cut

Nishio, M. , Kono, A. , Kubo, K. , Koyama, H. , Nishii, T. and Sugimura, K. (2015) Tumor Segmentation on ¹⁸F FDG-PET Images Using Graph Cut and Local Spatial Information. Open Journal of Medical Imaging, 5, 174-181. doi: 10.4236/ojmi.2015.53022.