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首页> 外文期刊>Journal of medical systems >Accurate automated detection of autism related corpus callosum abnormalities.
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Accurate automated detection of autism related corpus callosum abnormalities.

机译:精确地自动检测与孤独症相关的call体异常。

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摘要

The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.
机译:不能过分夸大对孤独症进行准确的早期诊断的重要性,这种诊断会严重影响个人行为和沟通技巧。神经病理学研究显示自闭症大脑的Call体(CC)解剖结构异常。我们提出了一种对大脑的三维(3D)磁共振图像(MRI)进行定量分析的新方法,该方法可确保对自闭症患者和正常受试者的CC之间的解剖差异进行更准确的量化。它包括三个主要处理步骤:(i)使用学习到的CC形状和视觉外观从给定的3D MRI分割CC; (ii)提取CC的中心线; (iii)CC曲面的圆柱映射,以进行比较分析。我们的实验揭示了17个正常受试者和17个自闭症受试者在四个解剖部位(即其CC的脾脏,讲台,膝和身体)之间存在显着差异(在95%置信水平上)。

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