Related Experiment Video
Updated: Apr 20, 2026
Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
Published on: July 29, 2022
Iterative probabilistic voxel labeling: automated segmentation for analysis of The Cancer Imaging Archive
T C Steed1, J M Treiber2, K S Patel3
1From the Neurosciences Graduate Program (T.C.S.) School of Medicine (T.C.S., J.M.T.) Center for Theoretical and Applied Neuro-Oncology, Division of Neurosurgery, Moores Cancer Center (T.C.S., J.M.T., K.S.P., Z.T., M.L.T., B.S.C., C.C.C.), University of California, San Diego, La Jolla, California.
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-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 ± 0.03 for contrast-enhancing volumes and 0.84 ± 0.09 for FLAIR hyperintensity volumes. These values compared favorably with the means of Dice similarity coefficients between the operator-defined segmentations: 0.92 ± 0.03 for contrast-enhancing volumes and 0.92 ± 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.
