Wang Rui, Li Chao, Wang Jie, Wei Xiaoer, Li Yuehua, Hui Chun, Zhu Yuemin, Zhang Su
School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China.
Institute of Diagnostic and Interventional Radiology, Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, Shanghai, China.
Acad Radiol. 2014 Dec;21(12):1512-23. doi: 10.1016/j.acra.2014.07.001. Epub 2014 Aug 28.
Quantitative analysis of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images provides information for disease tracking, therapeutic efficacy assessment, and cognitive science research. This study developed an automatic segmentation method to detect and quantify WMHs on FLAIR images. This study aims to assess the accuracy and reproducibility of the proposed method.
The FLAIR images of 82 patients (58-84 years) with different WMH burdens were acquired with the same 3T scanner (Intera-achieva SMI-2.1; Philip Medical System, Sixth Affiliated People's Hospital, Shanghai, China). The FLAIR images were preprocessed through brain extraction and intensity inhomogeneity correction. An anatomy atlas built from a set of 20 patients with different WMH burdens (mild, 11 patients; moderate, 6 patients; and severe, 3 patients) was used to estimate a control parameter in the framework of segmentation. The general flow for WMH segmentation included classification of foreground and background regions, detection of abnormally high signals, and WMH refinement. The performance of automatic segmentation was evaluated by a volumetric comparison with manual segmentation on patients with different WMH burdens.
Similarity index values for the accuracy of automatic segmentation compared to manual segmentation were consistently high for patients with different WMH burdens (mild, 0.78 ± 0.08; moderate, 0.83 ± 0.06; severe, 0.84 ± 0.08; and total, 0.80 ± 0.08). Linear regression demonstrated a strong correlation between the WMH volumes measured by the two methods in all patients (r = 0.98, P = .006). Small average differences were detected between the WMH volumes obtained through manual and automatic segmentations (mild, 4.76%; moderate, 6.84%; and severe, 7.59%). The results of Bland-Altman analysis show a system bias of 0.68 cm(3) and a standard deviation of 2.10 cm(3) over the range of 2.58-53.9 cm(3).
The developed method is accurate and efficient in detecting and quantifying differently sized WMHs on FLAIR images. Automatic segmentation is a promising computer-aided diagnostic tool to study WMHs in aging and dementia in basic research and even in clinical trials.
对液体衰减反转恢复（FLAIR）图像上的脑白质高信号（WMH）进行定量分析可为疾病跟踪、治疗效果评估及认知科学研究提供信息。本研究开发了一种自动分割方法，用于在FLAIR图像上检测和量化WMH。本研究旨在评估所提方法的准确性和可重复性。
使用同一台3T扫描仪（Intera-achieva SMI-2.1；飞利浦医疗系统，中国上海第六人民医院）采集了82例（年龄58 - 84岁）具有不同WMH负荷的患者的FLAIR图像。FLAIR图像经过脑提取和强度不均匀性校正进行预处理。由一组20例具有不同WMH负荷的患者（轻度，11例；中度，6例；重度，3例）构建的解剖图谱用于在分割框架内估计一个控制参数。WMH分割的一般流程包括前景和背景区域分类、异常高信号检测以及WMH细化。通过与不同WMH负荷患者的手动分割进行体积比较，评估自动分割的性能。
不同WMH负荷患者自动分割与手动分割准确性的相似性指数值始终较高（轻度，0.78 ± 0.08；中度，0.83 ± 0.06；重度，0.84 ± 0.08；总体，0.80 ± 0.08）。线性回归表明，两种方法测量的所有患者的WMH体积之间存在强相关性（r = 0.98，P = 0.006）。手动分割和自动分割获得的WMH体积之间检测到的平均差异较小（轻度，4.76%；中度，6.84%；重度，7.59%）。Bland-Altman分析结果显示，在2.58 - 53.9 cm³范围内系统偏差为0.68 cm³，标准差为2.10 cm³。
所开发的方法在检测和量化FLAIR图像上不同大小的WMH方面准确且高效。自动分割是一种有前景的计算机辅助诊断工具，可用于基础研究甚至临床试验中对衰老和痴呆中的WMH进行研究。
