Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA.
Alzheimers Dement. 2012 Sep;8(5):399-406. doi: 10.1016/j.jalz.2011.07.002.
The segmentation of brain structures on magnetic resonance imaging scans for calculating regional brain volumes, using automated anatomic labeling, requires the use of both brain atlases and templates (template sets). This study aims to improve the accuracy of volumetric analysis of hippocampus (HP) and amygdala (AMG) in the assessment of early Alzheimer's disease (AD) by developing template sets that correspond more closely to the brains of elderly individuals.
Total intracranial volume and HP and AMG volumes were calculated for elderly subjects with no cognitive impairment (n = 103), with amnestic mild cognitive impairment (n = 68), or with probable AD (n = 46) using the following: (1) a template set consisting of a standard atlas (atlas S), drawn on a young adult male brain, and the widely used Montreal Neurological Institute template (MNI template set); (2) a template set (template S set) in which the template is based on smoothing the image from which atlas S is derived; and (3) a new template set (template E set) in which the template is based on an atlas (atlas E) created from the brain of an elderly individual.
Correspondence to HP and AMG volumes derived from manual segmentation was highest with automated segmentation by template E set, intermediate with template S set, and lowest with the MNI template set. The areas under the receiver operating curve for distinguishing elderly subjects with no cognitive impairment from elderly subjects with amnestic mild cognitive impairment or probable AD and the correlations between HP and AMG volumes and cognitive and functional scores were highest for template E set, intermediate for template S set, and lowest for the MNI template set.
The accuracy of automated anatomic labeling and the diagnostic value of the derived volumes are improved with template sets based on brain atlases closely resembling the anatomy of the to-be-segmented brain magnetic resonance imaging scans.
使用自动解剖标记对磁共振成像扫描进行脑结构分割以计算区域脑体积，需要同时使用脑图谱和模板（模板集）。本研究旨在通过开发更接近老年人大脑的模板集来提高海马体（HP）和杏仁核（AMG）体积分析在早期阿尔茨海默病（AD）评估中的准确性。
使用以下方法计算无认知障碍（n=103）、有遗忘型轻度认知障碍（n=68）或可能 AD（n=46）的老年人的总颅内体积和 HP 和 AMG 体积：（1）由标准图谱（图谱 S）组成的模板集，绘制在年轻成年男性大脑上，以及广泛使用的蒙特利尔神经学研究所模板（MNI 模板集）；（2）模板集（模板 S 集），其中模板基于对源自图谱 S 的图像进行平滑处理；和（3）新模板集（模板 E 集），其中模板基于从老年人大脑创建的图谱（图谱 E）。
与手动分割得出的 HP 和 AMG 体积的对应程度最高的是自动分割的模板 E 集，其次是模板 S 集，最低的是 MNI 模板集。用于区分无认知障碍的老年人和有遗忘型轻度认知障碍或可能 AD 的老年人的接收者操作曲线下面积以及 HP 和 AMG 体积与认知和功能评分之间的相关性最高的是模板 E 集，其次是模板 S 集，最低的是 MNI 模板集。
与基于更接近要分割的大脑磁共振成像扫描的解剖结构的脑图谱的模板集相比，自动解剖标记的准确性和衍生体积的诊断价值得到了提高。
