@article {Steed, author = {T.C. Steed and J.M. Treiber and K.S. Patel and Z. Taich and N.S. White and M.L. Treiber and N. Farid and B.S. Carter and A.M. Dale and C.C. Chen}, title = {Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images}, year = {2014}, doi = {10.3174/ajnr.A4171}, publisher = {American Journal of Neuroradiology}, abstract = {BACKGROUND AND PURPOSE: Robust, automated segmentation algorithms are required for quantitative analysis of large imaging datasets. We developed an automated method that identifies and labels brain tumor{\textendash}associated pathology by using an iterative probabilistic voxel labeling using k-nearest neighbor and Gaussian mixture model classification. Our purpose was to develop a segmentation method which could be applied to a variety of imaging from The Cancer Imaging Archive. MATERIALS AND METHODS: Images from 2 sets of 15 randomly selected subjects with glioblastoma from The Cancer Imaging Archive were processed by using the automated algorithm. The algorithm-defined tumor volumes were compared with those segmented by trained operators by using the Dice similarity coefficient. RESULTS: Compared with operator volumes, algorithm-generated segmentations yielded mean Dice similarities of 0.92 {\textpm} 0.03 for contrast-enhancing volumes and 0.84 {\textpm} 0.09 for FLAIR hyperintensity volumes. These values compared favorably with the means of Dice similarity coefficients between the operator-defined segmentations: 0.92 {\textpm} 0.03 for contrast-enhancing volumes and 0.92 {\textpm} 0.05 for FLAIR hyperintensity volumes. Robust segmentations can be achieved when only postcontrast T1WI and FLAIR images are available. CONCLUSIONS: Iterative probabilistic voxel labeling defined tumor volumes that were highly consistent with operator-defined volumes. Application of this algorithm could facilitate quantitative assessment of neuroimaging from patients with glioblastoma for both research and clinical indications. Abbreviations BVblood vesselCEVcontrast-enhancing volumeDICEDice similarity coefficientFHVFLAIR hyperintensity volumeGMMGaussian mixture modelingIPVLiterative probabilistic voxel labelingKNNk-nearest neighborT1wCET1WI with contrast enhancementTCIAThe Cancer Imaging ArchiveTCGAThe Cancer Genome Atlas}, issn = {0195-6108}, URL = {https://www.ajnr.org/content/early/2014/11/20/ajnr.A4171}, eprint = {https://www.ajnr.org/content/early/2014/11/20/ajnr.A4171.full.pdf}, journal = {American Journal of Neuroradiology} }