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A novel CAD system for autism diagnosis using structural and functional MRI

机译:使用结构和功能MRI的新型自闭症诊断CAD系统

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This paper introduces a comprehensive computer-aided diagnosis (CAD) system for autism diagnosis that integrates anatomical and functional information of the brain using both structural and functional magnetic resonance (MR) brain images. In order to move towards the idea of personalized medicine, analysis of the brain's Brodmann areas (BAs) is conducted to reach a diagnosis decision on the local areas. This local analysis will help clinicians allocate the subject on the autism spectrum as well as correlate autism abnormalities to the areas responsible for certain skills, such as cognitive ones. The diagnosis is done through several analysis steps on both structural and functional MRI volumes. First, anatomical features are extracted from the cerebral cortex (Cx) and the cerebral white matter (CWM) of structural MR images. A monetary reward functional MR experiment is also held to get the areas of activation in the brains of the participants in response to the applied task. Next, all the extracted features are fed to a multi-level deep network for both local and global diagnosis. The CAD system has been evaluated using subjects from the NDAR database (23 - 210 months), achieving a global classification accuracy of 94.7% based on fusing both modalities. Moreover, brain maps are shown for different autistic subjects to indicate the strength of association of each BA with autism, which supports the idea of personalized medicine. The proposed CAD system, along with the idea of local feature extraction and diagnosis, holds the promise for being capable to resolve autism endophenotypes.
机译:本文介绍了一种用于自闭症诊断的综合计算机辅助诊断(CAD)系统,该系统使用结构和功能磁共振(MR)脑图像将大脑的解剖和功能信息整合在一起。为了朝着个性化医学的方向发展,对大脑的布罗德曼区域(BAs)进行了分析,以做出有关本地区域的诊断决定。这种局部分析将帮助临床医生根据自闭症谱系对受试者进行分配,并将自闭症异常与负责某些技能(例如认知技能)的区域相关联。通过对结构和功能MRI体积的几个分析步骤来完成诊断。首先,从结构MR图像的大脑皮层(Cx)和大脑白质(CWM)中提取解剖特征。还进行了金钱奖励功能性MR实验,以获取参与者对所应用任务做出反应的大脑激活区域。接下来,将所有提取的特征馈送到多层深度网络以进行本地和全局诊断。已使用NDAR数据库中的对象(23-210个月)对CAD系统进行了评估,基于两种方法的融合,全球分类精度达到94.7%。此外,显示了针对不同自闭症受试者的脑图,以表明每个BA与自闭症的关联强度,这支持了个性化医学的想法。提出的CAD系统以及局部特征提取和诊断的想法,有望解决自闭症的内表型。

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