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AJNR Am J Neuroradiol. 2015 Apr;36(4):678-85. doi: 10.3174/ajnr.A4171. Epub 2014 Nov 20.

Iterative probabilistic voxel labeling: automated segmentation for analysis of The Cancer Imaging Archive glioblastoma images.

Author information

1
From 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.
2
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.
3
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 Weill-Cornell Medical College (K.S.P.), New York Presbyterian Hospital, New York, New York.
4
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.
5
Multimodal Imaging Laboratory (N.S.W., N.F., A.M.D.).
6
Multimodal Imaging Laboratory (N.S.W., N.F., A.M.D.) Department of Radiology (N.F., A.M.D.).
7
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 clarkchen@ucsd.edu.

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-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.

PMID:
25414001
DOI:
10.3174/ajnr.A4171
[Indexed for MEDLINE]
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