Going deeper with brain morphometry using neural networks

TL;DR

This paper introduces HerstonNet, a 3D ResNet-based neural network that significantly improves the accuracy and efficiency of brain morphometry measurements from MRI, aiding neurodegenerative disease research.

Contribution

The paper presents a novel multi-scale 3D ResNet model with a robust optimization scheme for more accurate brain morphometry from MRI scans.

Findings

HerstonNet achieves 24.30% higher intraclass correlation with FreeSurfer standards.

The model maintains competitive run-time performance.

It effectively learns rich features directly from MRI data.

Abstract

Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convolutional neural networks can infer morphometric measurements within a few seconds. Nevertheless, the accuracy of the devised model for insightful bio-markers (mean curvature and thickness) remains unsatisfactory. In this paper, we propose a more accurate and efficient neural network model for brain morphometry named HerstonNet. More specifically, we develop a 3D ResNet-based neural network to learn rich features directly from MRI, design a multi-scale regression scheme by predicting morphometric measures at feature maps of different resolutions, and leverage a robust optimization method to avoid poor quality minima and reduce the prediction variance. As a result, HerstonNet improves the existing approach by 24.30% in terms of intraclass correlation coefficient (agreement measure) to FreeSurfer silver-standards while maintaining a competitive run-time.