论文标题
使用LSTM在儿童大脑MRI上使用LSTM进行脑年龄估计
Brain Age Estimation Using LSTM on Children's Brain MRI
论文作者
论文摘要
基于儿童大脑MRI的大脑年龄预测是大脑健康和大脑发育分析的重要生物标志物。在本文中，我们将3D脑MRI体积视为2D图像的序列，并使用复发性神经网络提出了一个新框架，以进行大脑年龄估计。所提出的方法被命名为2D-Resnet18+长短期内存（LSTM），该记忆由四个部分：2D Resnet18组成，用于2D图像上的特征提取，序列上的特征降低，LSTM层，LSTM层和最终回归层。我们将提出的方法应用于公共多站点NIH-PD数据集，并评估第二个多站点数据集上的概括，这表明所提出的2D-Resnet18+LSTM方法比传统的基于3D的神经网络对脑年龄估计提供了更好的结果。
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite NIH-PD dataset and evaluate generalization on a second multisite dataset, which shows that the proposed 2D-ResNet18+LSTM method provides better results than traditional 3D based neural network for brain age estimation.
