Automated white matter total lesion volume segmentation in diabetes.
Automated white matter total lesion volume segmentation in diabetes.
复制标题
DOI：
10.3174/ajnr.a3590
复制
发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
Bowden DW
中科院分区：
文献类型：
--
作者：
Maldjian JA;Whitlow CT;Saha BN;Kota G;Vandergriff C;Davenport EM;Divers J;Freedman BI;Bowden DW
WM病变分割通常使用主观评定量表进行，因为人工方法费力且繁琐；然而，自动化的方法现在是可用的。在一组2型糖尿病患者中，我们比较了使用自动WM病变分割算法计算的总病变体积分级与主观评定量表和专家手动分割的性能。50名糖尿病患者（年龄67.7±7.2岁）和50对非糖尿病兄弟姐妹（年龄67.5±9.4岁）的结构T1和FLAIR MR成像数据在一项机构审查委员会批准的研究中进行评估。通过统计参数映射（SPM8）病灶分割工具箱生成每个受试者的WM病灶分割图和病灶总体积。主观WM病变等级由2名读者通过0-9的评分量表确定。基底真病灶总体积由经验丰富的读者手工分割确定。相关性分析比较了人工分割病变总体积与自动和主观评价方法。病灶平均分割与病灶真值的相关系数为0.84。在分割阈值k = 0.25时，病灶分割工具箱与基底真病灶总体积（ρ = 0.87）的相关性最大，而在k = 0.15时，主观病灶分割工具箱与病灶分割工具箱之间的相关性最大（ρ = 0.73）。2个相关估计值与真实值的差异无统计学意义。较低的分割阈值（0.15 vs . 0.25）表明主观评分者高估了WM病变负担。我们验证了病变分割工具箱用于确定糖尿病富集人群的总病变体积，并将其与常见的主观WM病变评定量表进行比较。病变分割工具箱是一个很容易获得的替代主观WM病变评分在研究糖尿病和其他人群的变化与白化。
WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes. Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods. Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden. We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.