  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.

Quantitative Methods