Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.
Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.
复制标题
DOI：
10.1093/neuonc/nou159
复制
发表时间：
2015-03
期刊：
影响因子：
15.9
通讯作者：
Barrick TR
中科院分区：
文献类型：
--
作者：
Jones TL;Byrnes TJ;Yang G;Howe FA;Bell BA;Barrick TR
对非侵入性脑肿瘤生物标志物的需求日益增加，以指导手术和随后的肿瘤治疗。我们提出了一种新的全脑扩散张量成像（DTI）分割（D-SEG）描绘肿瘤体积的兴趣（VOIs），为后续的肿瘤类型分类。D-SEG使用扩散张量的各向同性（p）和各向异性（q）分量来分割具有相似扩散特征的区域。从95名低级别和高级别胶质瘤、转移瘤和脑膜瘤患者和29名健康受试者中获得DTI扫描。D-SEG使用2D（p，q）空间的k均值聚类来生成具有不同各向同性和各向异性扩散特性的片段。我们的结果可视化使用一种新的RGB颜色方案，将p，q和T2加权信息在每个部分。每段对灰质、白色物质和脑脊液空间的体积贡献用于生成健康组织D-SEG光谱。使用半自动洪水填充技术提取肿瘤VOI，并在VOI内计算D-SEG光谱。使用支持向量机进行使用D-SEG光谱的肿瘤类型分类。D-SEG在计算上是快速和稳定的，并且从肿瘤和水肿中描绘出健康组织的区域。每种肿瘤类型的D-SEG光谱是一致的，成分扩散特征可能反映了组织微观结构的区域差异。支持向量机分类肿瘤类型的总体准确率为94.7%，提供了比以前报道的更好的分类。D-SEG是一种用户友好的半自动生物标志物，可为非侵入性脑肿瘤诊断和治疗计划提供有价值的辅助手段。
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. 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. 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. D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning.
登录
查看更多内容
DOI：
10.1006/jmrb.1994.1037
发表时间：
1994-03-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子：
--
作者：
BASSER, PJ;MATTIELLO, J;LEBIHAN, D
通讯作者：
LEBIHAN, D
影响因子：
1.9
作者：
Inoue, T;Ogasawara, K;Kabasawa, H
通讯作者：
Kabasawa, H
影响因子：
2.9
作者：
Byrnes, Tiernan J. D.;Barrick, Thomas R.;Clark, Chris A.
通讯作者：
Clark, Chris A.
影响因子：
10.6
作者：
Clark, MC;Hall, LO;Silbiger, MS
通讯作者：
Silbiger, MS
影响因子：
19.7
作者：
Kaus, MR;Warfield, SK;Kikinis, R
通讯作者：
Kikinis, R