Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection.
Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection.
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DOI：
10.1002/nbm.1777
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发表时间：
2012-04
影响因子：
2.9
通讯作者：
Madabhushi, A.
中科院分区：
文献类型：
--
作者：
Tiwari, P.;Viswanath, S.;Kurhanewicz, J.;Sridhar, A.;Madabhushi, A.
关键词：
开发计算机化数据集成框架（MaWERiC），用于定量组合来自不同磁共振（MR）成像模式的结构和代谢信息。在本文中，我们提出了一个新的计算机支持系统，我们称之为多模态小波嵌入表示数据组合（MaWERiC），它（1）采用小波理论和降维提供了一个共同的，统一的表示不同的成像（T2-w）和非成像（光谱学）MRI通道，以及（2）利用随机森林分类器，根据组合的1.5特斯拉体内MRI和MRS，在每个体素的基础上自动检测前列腺癌。通过MaWERiC对36项1.5 T直肠内体内T2-w MRI、MRS患者研究进行了每体素评价，使用了25次迭代的三重交叉验证方案。通过离体整体组织学切片获得了用于评价结果的基础事实，该组织学切片作为放射科专家在每个体素基础上注释前列腺癌的金标准。结果表明，基于MaWERiC的MRS-T2-w元分类器（平均AUC，μ = 0.89 ± 0.02）显著优于（i）T2-w MRI（采用小波纹理特征）分类器（μ = 0.55± 0.02），（ii）MRS（采用代谢物比率）分类器（μ= 0.77 ± 0.03），（iii）决策融合分类器，通过组合个体T2-w MRI和MRS分类器输出获得（μ = 0.85 ± 0.03）和（iv）涉及代谢MRS和MR信号强度特征的组合的数据组合方案（μ = 0.66± 0.02）。提出了一种用于结合成像和非成像MRI通道的新型数据集成框架MaWERiC。与单独的T2-w MRI、MRS模式和其他数据集成策略相比，通过T2-w MRI和MRS数据组合应用于前列腺癌检测显示出显着更高的AUC和准确度值。
To develop a computerized data integration framework (MaWERiC) for quantitatively combining structural and metabolic information from different Magnetic Resonance (MR) imaging modalities. In this paper, we present a novel computerized support system that we call Multimodal Wavelet Embedding Representation for data Combination (MaWERiC) which (1) employs wavelet theory and dimensionality reduction for providing a common, uniform representation of the different imaging (T2-w) and non-imaging (spectroscopy) MRI channels, and (2) leverages a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 Tesla in vivo MRI and MRS. A total of 36 1.5 T endorectal in vivo T2-w MRI, MRS patient studies were evaluated on a per-voxel via MaWERiC, using a three-fold cross validation scheme across 25 iterations. Ground truth for evaluation of the results was obtained via ex-vivo whole-mount histology sections which served as the gold standard for expert radiologist annotations of prostate cancer on a per-voxel basis. The results suggest that MaWERiC based MRS-T2-w meta-classifier (mean AUC, μ = 0.89 ± 0.02) significantly outperformed (i) a T2-w MRI (employing wavelet texture features) classifier (μ = 0.55± 0.02), (ii) a MRS (employing metabolite ratios) classifier (μ= 0.77 ± 0.03), (iii) a decision-fusion classifier, obtained by combining individual T2-w MRI and MRS classifier outputs (μ = 0.85 ± 0.03) and (iv) a data combination scheme involving combination of metabolic MRS and MR signal intensity features (μ = 0.66± 0.02). A novel data integration framework, MaWERiC, for combining imaging and non-imaging MRI channels was presented. Application to prostate cancer detection via combination of T2-w MRI and MRS data demonstrated significantly higher AUC and accuracy values compared to the individual T2-w MRI, MRS modalities and other data integration strategies.
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影响因子：
2.5
作者：
COIFMAN, RR;WICKERHAUSER, MV
通讯作者：
WICKERHAUSER, MV
影响因子：
6.6
作者：
Borboroglu, PG;Comer, SW;Amling, CL
通讯作者：
Amling, CL
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
影响因子：
3
作者：
Díaz-Uriarte R;Alvarez de Andrés S
通讯作者：
Alvarez de Andrés S