Brain Age Estimation using Brain MRI and 3D Convolutional Neural Network

Nastsrsn Pardakhti, H. Sajedi
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引用次数: 4

Abstract

Human Brain Age has become a popular aging biomarker and is used to detect the differences among healthy subjects. It is also used as a health biomarker between the group of normal subjects and the group of patients. Machine Learning (ML) prediction models and especially Deep Learning (DL) systems are rapidly grown up in the field of Brain Age Estimation (BAE) to present a disease detection system. In this paper, a DL method based on 3D-CNN is designed to get an accurate result of BAE. The training dataset is selected from the IXI (Information eXtraction from Images) MRI data repository. In addition, it is aimed to decrease the computations required by the deep model on the 3D MRI images. It is generally done by removing the unnecessary parts of brain 3D images. First, the deep 3D-CNN model is trained by healthy MRI data of IXI dataset which are normalized by SPM. Next, some experiments are done due to decrease the computations while saving the total performance. The best-achieved Mean Absolute Error (MAE) is 5.813 years.
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基于脑MRI和三维卷积神经网络的脑年龄估计
人脑年龄已成为一种流行的衰老生物标志物,用于检测健康受试者之间的差异。它也被用作正常受试者组和患者组之间的健康生物标志物。机器学习(ML)预测模型,特别是深度学习(DL)系统在脑年龄估计(BAE)领域迅速发展,呈现出一种疾病检测系统。本文设计了一种基于3D-CNN的深度学习方法,以获得准确的BAE结果。训练数据集选自IXI (Information eXtraction from Images) MRI数据库。此外,该方法还旨在减少深度模型对三维MRI图像的计算量。它通常是通过去除大脑3D图像中不必要的部分来完成的。首先,采用SPM归一化后的IXI数据集的健康MRI数据训练深度3D-CNN模型;其次,为了在节省总性能的同时减少计算量,进行了一些实验。最佳平均绝对误差(MAE)为5.813年。
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