Arimura Hidetaka, Yoshiura Takashi, Kumazawa Seiji, Tanaka Kazuhiro, Koga Hiroshi, Mihara Futoshi, Honda Hiroshi, Sakai Shuji, Toyofuku Fukai, Higashida Yoshiharu
Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Fukuoka 812-8582, Japan.
Acad Radiol. 2008 Mar;15(3):274-84. doi: 10.1016/j.acra.2007.10.020.
An automated method for identification of patients with cerebral atrophy due to Alzheimer's disease (AD) was developed based on three-dimensional (3D) T1-weighted magnetic resonance (MR) images.
Our proposed method consisted of determination of atrophic image features and identification of AD patients. The atrophic image features included white matter and gray matter volumes, cerebrospinal fluid (CSF) volume, and cerebral cortical thickness determined based on a level set method. The cortical thickness was measured with normal vectors on a voxel-by-voxel basis, which were determined by differentiating a level set function. The CSF spaces within cerebral sulci and lateral ventricles (LVs) were extracted by wrapping the brain tightly in a propagating surface determined with a level set method. Identification of AD cases was performed using a support vector machine (SVM) classifier, which was trained by the atrophic image features of AD and non-AD cases, and then an unknown case was classified into either AD or non-AD group based on an SVM model. We applied our proposed method to MR images of the whole brains obtained from 54 cases, including 29 clinically diagnosed AD cases (age range, 52-82 years; mean age, 70 years) and 25 non-AD cases (age range, 49-78 years; mean age, 62 years).
As a result, the area under a receiver operating characteristic (ROC) curve (Az value) obtained by our computerized method was 0.909 based on a leave-one-out test in identification of AD cases among 54 cases.
This preliminary result showed that our method may be promising for detecting AD patients.
基于三维（3D）T1加权磁共振（MR）图像，开发了一种用于识别阿尔茨海默病（AD）所致脑萎缩患者的自动化方法。
我们提出的方法包括萎缩图像特征的确定和AD患者的识别。萎缩图像特征包括白质和灰质体积、脑脊液（CSF）体积以及基于水平集方法确定的脑皮质厚度。皮质厚度在逐个体素的基础上用法向量测量，法向量通过对水平集函数求导来确定。脑沟和侧脑室（LVs）内的CSF间隙通过用水平集方法确定的传播表面紧密包裹大脑来提取。AD病例的识别使用支持向量机（SVM）分类器，该分类器通过AD和非AD病例的萎缩图像特征进行训练，然后根据SVM模型将未知病例分类为AD组或非AD组。我们将我们提出的方法应用于从54例患者获得的全脑MR图像，包括29例临床诊断为AD的病例（年龄范围52 - 82岁；平均年龄70岁）和25例非AD病例（年龄范围49 - 78岁；平均年龄62岁）。
结果，在54例病例中识别AD病例的留一法测试中，我们的计算机化方法获得的受试者操作特征（ROC）曲线下面积（Az值）为0.909。
这一初步结果表明我们的方法在检测AD患者方面可能很有前景。
