Molecular Imaging and Biology

, Volume 19, Issue 3, pp 456–468 | Cite as

A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

  • Motahare Soufi
  • Alireza Kamali-AslEmail author
  • Parham Geramifar
  • Arman RahmimEmail author
Research Article



Determination of intra-tumor high-uptake area using 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [18F]FDG-PET with the capability of intra-tumor high-uptake region delineation.


We utilized and extended a fuzzy random walk PET tumor segmentation algorithm to delineate intra-tumor high-uptake areas. Tumor textural feature (TF) analysis was used to find a relationship between tumor type and TF values. Segmentation accuracy was evaluated quantitatively utilizing 70 clinical [18F]FDG-PET lung images of patients with a total of 150 solid tumors. For volumetric analysis, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) measures were extracted with respect to gold-standard manual segmentation. A multi-linear regression model was also proposed for automated tumor labeling based on TFs, including cross-validation analysis.


Two-tailed t test analysis of TFs between homogeneous and heterogeneous tumors revealed significant statistical difference for size-zone variability (SZV), intensity variability (IV), zone percentage (ZP), proposed parameters II and III, entropy and tumor volume (p < 0.001), dissimilarity, high intensity emphasis (HIE), and SUVmin (p < 0.01). Lower statistical differences were observed for proposed parameter I (p = 0.02), and no significant differences were observed for SUVmax and SUVmean. Furthermore, the Spearman rank analysis between visual tumor labeling and TF analysis depicted a significant correlation for SZV, IV, entropy, parameters II and III, and tumor volume (0.68 ≤ ρ ≤ 0.84) and moderate correlation for ZP, HIE, homogeneity, dissimilarity, parameter I, and SUVmin (0.22 ≤ ρ ≤ 0.52), while no correlations were observed for SUVmax and SUVmean (ρ < 0.08). The multi-linear regression model for automated tumor labeling process resulted in R 2 and RMSE values of 0.93 and 0.14, respectively (p < 0.001), and generated tumor labeling sensitivity and specificity of 0.93 and 0.89. With respect to baseline random walk segmentation, the results showed significant (p < 0.001) mean DSC, HD, and SUVmean error improvements of 21.4 ± 11.5 %, 1.4 ± 0.8 mm, and 16.8 ± 8.1 % in homogeneous tumors and 7.4 ± 4.4 %, 1.5 ± 0.6 mm, and 7.9 ± 2.7 % in heterogeneous lesions. In addition, significant (p < 0.001) mean DSC, HD, and SUVmean error improvements were observed for tumor sub-volume delineations, namely 5 ± 2 %, 1.5 ± 0.6 mm, and 7 ± 3 % for the proposed Fuzzy RW method compared to RW segmentation.


We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [18F]FDG-PET images.

Key words

Heterogeneous tumor delineation Automated PET image segmentation Random walk Fuzzy logic 


Compliance with Ethical Standards

Conflict of Interest

There authors declare they have no conflict of interest.


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Copyright information

© World Molecular Imaging Society 2016

Authors and Affiliations

  1. 1.Department of Radiation Medicine EngineeringShahid Beheshti UniversityTehranIran
  2. 2.Research Center for Nuclear Medicine, Shariati HospitalTehran University of Medical SciencesTehranIran
  3. 3.Department of RadiologyJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Electrical & Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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