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Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique

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Title: Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique
Authors: Jones, TL
Byrnes, TJ
Yang, G
Howe, FA
Bell, BA
Barrick, TR
Item Type: Journal Article
Abstract: Background There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Results Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning.
Issue Date: 13-Aug-2014
Date of Acceptance: 7-Jul-2014
URI: http://hdl.handle.net/10044/1/43299
DOI: https://dx.doi.org/10.1093/neuonc/nou159
ISSN: 1522-8517
Publisher: Oxford University Press
Start Page: 466
End Page: 476
Journal / Book Title: Neuro-Oncology
Volume: 17
Issue: 3
Copyright Statement: © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Keywords: Oncology & Carcinogenesis
1109 Neurosciences
1112 Oncology And Carcinogenesis
Publication Status: Published
Open Access location: http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4483092&blobtype=pdf
Appears in Collections:National Heart and Lung Institute