Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

TL;DR

This paper introduces a machine learning approach using deep neural networks to predict cortical thickness and curvature from MRI images, offering a faster and more robust alternative to traditional surface mesh analysis, especially in clinical settings.

Contribution

The paper presents a novel deep learning architecture that predicts cortical metrics directly from MRI images and estimates uncertainty, improving robustness over traditional surface-based methods.

Findings

Deep CNNs can accurately predict cortical metrics from MRI.

The method performs well on clinical cohorts with cortical surface modeling failures.

Uncertainty estimation enhances the reliability of predictions.

Abstract

The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.