基于深度网络集成的脑MRI脑年龄估计

Z. Jahanshiri, M. S. Abadeh, H. Sajedi
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引用次数: 0

摘要

脑生物年龄的估计是近年来备受关注的话题之一。其中一个最重要的原因是,通过脑年龄估计(BAE)可以早期发现神经退行性疾病,如阿尔茨海默氏症和帕金森症。脑成像是估计大脑生物年龄最重要的数据之一。由于大脑的自然衰老遵循一种特定的模式,这使得研究人员和医生能够通过大脑的退化来预测人类大脑的年龄。为此目的,已经对2D或3D脑图像数据进行了一些研究。本研究将三维卷积神经网络(cnn)和二维卷积神经网络(cnn)集成到BAE系统中。所提出的集成CNN (ECNN)方法的平均绝对误差(MAE)为3.57年,优于以往的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Age Estimation based on Brain MRI by an Ensemble of Deep Networks
Estimation of biological brain age is one of the topics that has been much discussed in recent years. One of the most important reasons for this is the possibility of early detection of neurodegenerative disorders such as Alzheimer's and Parkinson's with Brain Age Estimation (BAE). Brain imaging is one of the most important data to estimate the biological age of the brain. Because the brain's natural aging follows a particular pattern, it enables researchers and physicians to predict the human brain's age from its degeneration. Some studies have been done on 2D or 3D brain images data for this purpose. In this study, an ensemble structure, including 3D and 2D Convolutional Neural Networks (CNNs), is used to BAE. The proposed ensemble CNN (ECNN) method obtained a Mean Absolute Error (MAE) of 3.57 years, which is better than the previous studies.
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