Brain Age Estimation Using LSTM on Children's Brain MRI

Sheng He; Randy L. Gollub; Shawn N. Murphy; Juan David Perez; Sanjay Prabhu; Rudolph Pienaar; Richard L. Robertson; P. Ellen Grant; Yangming Ou

Brain Age Estimation Using LSTM on Children's Brain MRI

Computer Vision and Pattern Recognition 2020-02-24 v1

Authors: Sheng He , Randy L. Gollub , Shawn N. Murphy , Juan David Perez , Sanjay Prabhu , Rudolph Pienaar , Richard L. Robertson , P. Ellen Grant , Yangming Ou

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Abstract

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.

Keywords

brain imaging long short-term memory

Cite

@article{arxiv.2002.09045,
  title  = {Brain Age Estimation Using LSTM on Children's Brain MRI},
  author = {Sheng He and Randy L. Gollub and Shawn N. Murphy and Juan David Perez and Sanjay Prabhu and Rudolph Pienaar and Richard L. Robertson and P. Ellen Grant and Yangming Ou},
  journal= {arXiv preprint arXiv:2002.09045},
  year   = {2020}
}

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ISBI 2020

Brain Age Estimation Using LSTM on Children's Brain MRI

Abstract

Keywords

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