Automatic processing of diffusion-weighted ischemic stroke images based on divergence measures: slice and hemisphere identification, and stroke region segmentation
Automatic processing of diffusion-weighted ischemic stroke images based on divergence measures: slice and hemisphere identification, and stroke region segmentation
复制标题
DOI：
10.1007/s11548-008-0260-3
复制
发表时间：
2008-12-01
影响因子：
3
通讯作者：
Nowinski, Wieslaw L.
中科院分区：
文献类型：
--
作者：
Prakash, K. N. Bhanu;Gupta, Varsha;Nowinski, Wieslaw L.
目的 快速、准确、自动分割急性缺血性卒中病变对于临床试验很重要，并且具有高效卒中管理的潜力。为了在扩散加权磁共振成像 (DWI) 中识别中风切片、中风半球和分割中风区域，提出了基于散度的算法。 材料和方法 该研究使用了 57 个 DWI 体积，平面分辨率为 0.94-2.42 mm 内和 5-14 mm 内。我们使用强度概率密度函数的比率（pdf）作为散度度量。对于切片识别，该度量是切片的pdf与体积的比率；对于半球和梗死分割，它是左半球和右半球的pdf之差与其pdf之和的比率。中值和交叉点是切片和区域分割的阈值，而半球识别是无阈值的。进行描述性统计并进行ROC分析。结果分割的中位敏感性、特异性和Dice统计指数分别为86.34%、99.83%、0.72。对于切片和半球识别的敏感性和特异性分别为（90.05％；68.78％）和（94.74％；94.74％）。在 VC++ 中实现的算法每个体积需要 3-5 秒。 结论 这种自动、准确和快速的方法在临床环境和临床试验中可能有用，可定位和量化中风区域，消除内部和内部变异性，以及费力且耗时、依赖于操作员的手动分割。
Objective Fast, accurate and automatic segmentation of acute ischemic stroke lesions is important for clinical trials and has potential for efficient stroke management. To identify stroke slices, stroke hemisphere, and segment stroke regions in diffusion-weighted magnetic resonance imaging (DWI), divergence based algorithms are proposed.Materials and methods The study used 57 DWI volumes with inter 0.94-2.42 mm and intra 5-14 mm plane resolutions. We used ratio of intensity probability density functions (pdf) as a divergence measure. For slice identification, this measure is the ratio of pdfs of slice and the volume; for hemisphere and infarct segmentation, it is the ratio of the difference of the pdfs of the left and right hemispheres to the sum of their pdfs. The median and cross over points are thresholds for slice and region segmentation while hemisphere identification is threshold free. Descriptive statistics were determined and ROC analysis was performed.Results The median sensitivity, specificity, and Dice statistical index for segmentation are 86.34%, 99.83%, 0.72, respectively. For slice and hemisphere identification sensitivity and specificity are (90.05%; 68.78%) and (94.74%; 94.74%), respectively. The algorithm implemented in VC++ takes 3-5 s per volume.Conclusion This automatic, accurate and fast method is potentially useful in clinical setting and clinical trials to localize and quantify the stroke regions, eliminate inter- and intra-variability, and laborious and time consuming, operator-dependent manual segmentation.