Diffusion-weighted imaging-based probabilistic segmentation of high- and low-proliferative areas in high-grade gliomas
Diffusion-weighted imaging-based probabilistic segmentation of high- and low-proliferative areas in high-grade gliomas
复制标题
DOI：
10.1102/1470-7330.2012.0010
复制
发表时间：
2012-04-05
期刊：
影响因子：
4.9
通讯作者：
Stieltjes, Bram
中科院分区：
文献类型：
--
作者：
Simon, Dirk;Fritzsche, Klaus H.;Stieltjes, Bram
扩散加权成像（DWI）的表观扩散系数（ADC）与肿瘤增殖率呈负相关。高级别胶质瘤通常是异质性的，并且部分体积效应和模糊的边界阻碍了高和低增殖区域的描绘。通常使用的手动描绘进一步受到脑脊液和坏死的潜在重叠的阻碍。在这里，我们提出了一种算法，可重复地描绘和概率量化的ADC在异质性胶质瘤的高和低增殖的地区，导致在组织不均匀性的区域可重复的量化。我们使用的期望最大化（EM）聚类算法，应用于高斯混合模型，由纯叠加的高斯分布。在10例胶质瘤患者中评价了该方法的可靠性和可重复性。使用聚类发现的高和低增殖区域与使用所有可用成像数据绘制的保守感兴趣区域对应良好。模型初始化种子的系统放置显示了该方法的良好再现性。此外，我们说明了一个自动初始化的方法，完全消除用户引起的变化。总之，我们提出了一种快速，重复性好，自动化的方法来分离和定量胶质瘤中的异质区域。
The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) correlates inversely with tumor proliferation rates. High-grade gliomas are typically heterogeneous and the delineation of areas of high and low proliferation is impeded by partial volume effects and blurred borders. Commonly used manual delineation is further impeded by potential overlap with cerebrospinal fluid and necrosis. Here we present an algorithm to reproducibly delineate and probabilistically quantify the ADC in areas of high and low proliferation in heterogeneous gliomas, resulting in a reproducible quantification in regions of tissue inhomogeneity. We used an expectation maximization (EM) clustering algorithm, applied on a Gaussian mixture model, consisting of pure superpositions of Gaussian distributions. Soundness and reproducibility of this approach were evaluated in 10 patients with glioma. High-and low-proliferating areas found using the clustering correspond well with conservative regions of interest drawn using all available imaging data. Systematic placement of model initialization seeds shows good reproducibility of the method. Moreover, we illustrate an automatic initialization approach that completely removes user-induced variability. In conclusion, we present a rapid, reproducible and automatic method to separate and quantify heterogeneous regions in gliomas.