Probabilistic segmentation of white lesions in MR imaging
Probabilistic segmentation of white lesions in MR imaging
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DOI：
10.1016/j.neuroimage.2003.10.012
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发表时间：
2004-03-01
期刊：
影响因子：
5.7
通讯作者：
van der Grond, J
中科院分区：
文献类型：
--
作者：
Anbeek, P;Vincken, KL;van der Grond, J
提出了一种全自动分割颅脑磁共振成像中白色病变的新方法。该算法使用来自T1加权（T1-w）、反转恢复（IR）、质子密度加权（PD）、T2加权（T2-w）和液体衰减反转恢复（FLAIR）扫描的信息。它基于K-最近邻（KNN）分类技术，该技术从体素强度和空间信息构建特征空间。该技术生成表示每个体素作为WML的一部分的概率的图像。通过在这些概率图上应用阈值，可以获得二进制分割。ROC曲线表明，分割实现了高灵敏度和特异性。计算相似性指数（SI）、重叠分数（OF）和额外分数（EF），用于结果的额外定量分析。SI还用于确定用于生成二进制分割的最佳概率阈值。使用SI、OF和EF的概率等价物，可以直接评估概率图，为不同分类结果的比较提供了强有力的工具。这种用于自动WML分割的方法达到了与用于多发性硬化（MS）病变分割的方法相当的准确度，并且适合于在大型和纵向人群研究中检测WML。(C)2004年爱思唯尔公司All rights reserved.
A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies. (C) 2004 Elsevier Inc. All rights reserved.