Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment
Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment
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DOI：
10.1016/j.neuroimage.2005.05.005
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发表时间：
2005-10-01
期刊：
影响因子：
5.7
通讯作者：
Liu, YX
中科院分区：
文献类型：
--
作者：
Carmichael, OT;Aizenstein, HA;Liu, YX
本研究评估了公共领域的自动化方法的性能为基础的MRI分割的老年受试者与阿尔茨海默病（AD）和轻度认知障碍（MCI）的海马。匹兹堡大学阿尔茨海默病研究中心收集了54名年龄和性别匹配的健康老年人、可能患有AD的受试者和MCI受试者的结构MR图像。通过使用AIR、SPM、FLIRT和Chen的完全变形方法自动分割受试者图像中的海马，以将图像与哈佛图谱、MNI图谱和随机选择的手动标记的受试者图像（“队列图谱”）对齐。混合效应统计模型分析了大脑一侧、疾病状态、配准方法、图谱选择和手动跟踪协议对自动分割和专家手动分割之间空间重叠的影响。产生更高程度的几何变形的配准方法产生与手动分割具有更高一致性的自动分割。大脑侧面、AD的存在、参考图像的选择和手动跟踪协议也是影响自动分割性能的重要因素。在这个困难的分割任务上，完全自动化的技术可以与人类评分员竞争，但严格的统计分析表明，必须仔细考虑各种方法因素，以确保自动化方法在实践中表现良好。建议使用完全可变形配准方法、队列图谱和用户定义的手动描记，以实现全自动海马分割的最高性能。(C)2005年爱思唯尔公司All rights reserved.
This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation. (C) 2005 Elsevier Inc. All rights reserved.