# Brain Age Estimation Using LSTM on Children's Brain MRI

arXiv: https://arxiv.org/abs/2002.09045 · HTML: https://mlsanity.com/paper/2002.09045/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Relation Learning for Regression and Its Application to Brain Age Estimation](https://mlsanity.com/paper/2204.06598/) (2022)
- [Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI](https://mlsanity.com/paper/2108.01659/) (2021)
- [Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging](https://mlsanity.com/paper/2403.05702/) (2024)

## Abstract (arXiv)

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.