Convolutional Neural Networks for Estimation of Myelin Maturation in Infant Brain

TL;DR

This study develops a convolutional neural network to estimate infant brain myelination age from MRI images, achieving high correlation with actual age and demonstrating the potential of deep learning in neurodevelopmental assessment.

Contribution

The paper introduces an 8-layer CNN model that accurately estimates infant brain myelination age from MRI, with improved performance using ensemble learning across different age ranges.

Findings

Strong correlation (r=0.81) between estimated and actual age.

Ensemble learning further improved correlation to 0.93.

Deep learning effectively estimates myelination in infant brains.

Abstract

Myelination plays an important role in the neurological development of infant brain and MRI can visualize the myelination extension as T1 high and T2 low signal intensity at white matter. We tried to construct a convolutional neural network machine learning model to estimate the myelination. Eight layers CNN architecture was constructed to estimate the subjects age with T1 and T2 weighted image at 5 levels associated with myelin maturation in 119 subjects up to 24 months. CNN model learned with all age dataset revealed a strong correlation between the estimated age and the corrected age and the coefficient of correlation, root mean square error and mean absolute error was 0. 81, 3. 40 and 2. 28. Moreover, the adaptation of ensemble learning models with two datasets 0 to 16 months and 8 to 24 months improved that to 0. 93, 2. 12 and 1. 34. Deep learning can be adaptable to myelination estimation in infant brain.