Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.
Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.
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DOI：
10.1016/j.compmedimag.2009.04.006
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发表时间：
2009-09
影响因子：
5.7
通讯作者：
Wong, Stephen T. C.
中科院分区：
文献类型：
--
作者：
Nie, Jingxin;Xue, Zhong;Liu, Tianming;Young, Geoffrey S.;Setayesh, Kian;Guo, Lei;Wong, Stephen T. C.
针对多通道序列中的脑肿瘤分割问题，已经提出了多种算法，但大多数算法都要求图像的分辨率为各向同性或伪各向同性。尽管可以在分割之前执行低分辨率序列（例如T2加权图像）到高分辨率图像（例如T1加权图像）的空间上的共配准和内插，但是由于低分辨率图像的内插，结果通常受到部分体积效应的限制。为了提高肿瘤分割的质量，在临床应用中，低分辨率序列通常与高分辨率图像一起使用，我们提出了基于空间精度加权隐马尔可夫随机场和期望最大化（SHE）方法的自动肿瘤和增强肿瘤分割算法。SHE将低分辨率图像的空间插值精度纳入隐马尔可夫随机场（HMRF）的优化过程中，以使用具有不同分辨率的多通道MR图像分割肿瘤，例如，高分辨率T1加权和低分辨率T2加权图像。在实验中，我们使用一组具有已知真实组织分割的模拟多通道脑MR图像来评估该算法，并将其应用于脑肿瘤化疗临床试验期间获得的MR图像数据集。实验结果表明，与传统的多通道分割算法相比，该算法能够获得更准确的肿瘤分割结果。
A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.
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影响因子：
45.3
作者：
Samnick, S;Bader, JB;Kirsch, CM
通讯作者：
Kirsch, CM
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
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
影响因子：
56.9
作者：
ROGERS, DJ;TANIMOTO, TT
通讯作者：
TANIMOTO, TT