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Measurement of brain compartment volumes from MRI data using region growing and mixed-volume methods

机译:利用区域生长和混合体积方法测量来自MRI数据的脑室内体积

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MRI data were collected from normal control subjects and from patients having AIDS or closed head injury (CHI). Both T1- weighted and T2-weighted images were collected; in some cases transverse images were made, while in others coronal images were collected. The images were analyzed in three ways, each of which determined white matter (WM), gray matter (GM), and CSF volume, and the results were compared. Segmentation by interactive threshold selection or manual outlining is still the most commonly used method for compartment volume analysis. This method was applied to the control and AIDS brains, with repetition by two trained observers. These results were compared with those from a second method in which each voxel is viewed statistically as a mixture of whichever two compartments are closest at that anatomical location. Automatic thresholding, followed by regional skeletonization, is used to identify a small set of pixels in each slice that represent the central portion of each of the three regions of interest. All pixels in the slice are then subjected to an interpolation procedure in which their fractional composition is determined from these three intensity values. The intensities from the T1-weighted images show contrast between GM, WM, and CSF, and these are used for the volume computation. Geometric information from the T2-weighted image is used for the location of the authentic compartment locations, as these images show strong contrast between the CSF and the skull. The third method studied is based on a three-dimensional region-growing algorithm that estimates each volume compartment by growing a volume from a seed point, limiting the growth based on spatial and feature criteria. The feature bounds are set restrictively so that GM, WM, and CSF regions are not contiguous, leaving a volume of mixed voxels between the regions. The distributions of intensities in each region are then used to interpolate the most likely composition of the unassigned voxels, so that volume mixing is assumed only in the spaces between the assigned regions. This method is quite robust, requiring little operator judgement. The volumes obtained by these methods are not substantially different, and the methods differ primarily with respect to interoperator variability and convenience of use. The third method also differs from the others in that it treats the set of slices as a single 3-dimensional data set, making better use of regional information. All methods reveal significant changes in brain compartment volume in cases of CNS pathology.
机译:MRI数据从正常对照受试者和来自具有AIDS或闭合性颅脑损伤(CHI)患者采集。既T1-加权并收集T2加权图像;在某些情况下,横向图像进行了改造,而在其他国家收集冠状图像。这些图像有三种方式,其中的每一个确定的白质(WM),灰质(GM),和CSF体积进行分析,并将结果进行比较。分割通过交互式阈值选择或人工勾画轮廓仍然是室容积分析的最常用的方法。此方法是由两个训练观察者施加到控制和艾滋病的大脑,用重复。这些结果与那些从其中每个体素被统计学视为混合物为准的两个隔室都在该解剖位置最接近的第二方法进行了比较。自动阈值,随后的区域骨架化,用来识别在每个表示每个感兴趣的三个区域的中心部分切片小组像素。然后在切片中的所有像素进行,其中它们的分数组合物由这三种强度值确定的内插过程。从T1加权图像的强度表明GM,WM,和CSF之间的对比度,并且这些被用于体积计算。从T2加权的图像几何信息被用于真实隔室的位置的位置,因为这些图像显示了CSF和颅骨之间强烈的对比。研究的第三种方法是基于通过从种子点生长的体积,从而限制了基于空间和特征的标准生长估计每个隔室容积的三维区域生长算法。特征边界被设置为限制性,使得GM,WM,和CSF区域是不连续的,使区之间的体素混合的体积。在每个区域中强度的分布随后被用于内插的未分配的体素的最有可能的组合物,从而使体积混合假定仅在所分配的区域之间的空间。这种方法是相当强大的,只需要很少的操作人员的判断。通过这些方法获得的体积基本上没有不同,并且这些方法相对于操作符之间的变异性和使用便利性,主要不同。第三种方法也不同之处在于它将该组切片为单个的三维数据集,更好地利用区域信息的其他人。所有方法揭示大脑厢容积在CNS病理的情况下显著变化。

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