  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.
