A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

TL;DR

This paper introduces an automated, multi-contrast MRI segmentation tool for the corpus callosum, enabling shape analysis, reliability assessment, and genetic association studies in large datasets.

Contribution

The paper presents a novel multi-contrast MRI segmentation tool for the corpus callosum, including quality control and genetic analysis capabilities, applicable to diverse MRI qualities.

Findings

High segmentation reliability demonstrated with ICC and Dice scores.

Effective classification of shape abnormalities in large population data.

Genetic associations identified with callosal shape features.

Abstract

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.