Parsimonious model selection for tissue segmentation and classification applications:: A study using simulated and experimental DTI data
Parsimonious model selection for tissue segmentation and classification applications:: A study using simulated and experimental DTI data
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DOI：
10.1109/tmi.2007.907294
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发表时间：
2007-11-01
影响因子：
10.6
通讯作者：
Basser, Peter J.
中科院分区：
文献类型：
--
作者：
Freidlin, Raisa Z.;Oezarslan, Evren;Basser, Peter J.
这项工作的目的之一是调查的可行性，使用层次模型来描述扩散张量磁共振（MR）数据在固定的组织。简约的模型选择标准用于在组织内的不同扩散模型中进行选择。使用这些信息，我们评估我们是否可以同时进行组织分割和分类。从离体猪脊髓获得的数值模型和弥散加权成像（DWI）数据被用来测试和验证这个模型选择框架。三个层次的方法用于简约模型选择：施瓦茨准则（SC），F-检验t-检验（F-t），提出了Hext，和F-检验F-检验（F-F），改编自Snedecor。对于在各向同性和一般各向异性（全张量）模型之间进行选择，F-t方法比其他方法更鲁棒。然而，由于F-F和SC对方差估计和特征值排序偏差的高度敏感性，因此它是分割具有横向各向同性（圆柱对称）的模型的首选方法。此外，SC方法比F-t和F-F方法更容易实现，并且具有更好的性能.因此，这种方法可以有效地用于评估大型MRI数据集。此外，所提出的逐体素分割框架不容易受到由具有不同程度的各向异性的相邻体素中的方差的不均匀性引起的伪影的影响，这可能会污染利用基于体素平均的技术获得的分割结果。
One aim of this work is to investigate the feasibility of using a hierarchy of models to describe diffusion tensor magnetic resonance (MR) data in fixed tissue. Parsimonious model selection criteria are used to choose among different models of diffusion within tissue. Using this information, we assess whether we can perform simultaneous tissue segmentation and classification. Both numerical phantoms and diffusion weighted imaging (DWI) data obtained from excised pig spinal cord are used to test and validate this model selection framework. Three hierarchical approaches are used for parsimonious model selection: the Schwarz criterion (SC), the F-test t-test (F-t), proposed by Hext, and the F-test F-test (F-F), adapted from Snedecor. The F - t approach is more robust than the others for selecting between isotropic and general anisotropic (full tensor) models. However, due to its high sensitivity to the variance estimate and bias in sorting eigenvalues, the F - F and SC are preferred for segmenting models with transverse isotropy (cylindrical symmetry). Additionally, the SC method is easier to implement than the F - t and F - F methods and has better performance. As such, this approach can be efficiently used for evaluating large MRI data sets. In addition, the proposed voxel-by-voxel segmentation framework is not susceptible to artifacts caused by the inhomogeneity of the variance in neighboring voxels with different degrees of anisotropy, which might contaminate segmentation results obtained with the techniques based on voxel averaging.