  Manual segmentation of medical images is labor intensive and especially
challenging for images with poor contrast or resolution. The presence of
disease exacerbates this further, increasing the need for an automated
solution. To this extent, SynthSeg is a robust deep learning model designed for
automatic brain segmentation across various contrasts and resolutions. This
study validates the SynthSeg robust brain segmentation model on computed
tomography (CT), using a multi-center dataset. An open access dataset of 260
paired CT and magnetic resonance imaging (MRI) from radiotherapy patients
treated in 5 centers was collected. Brain segmentations from CT and MRI were
obtained with SynthSeg model, a component of the Freesurfer imaging suite.
These segmentations were compared and evaluated using Dice scores and Hausdorff
95 distance (HD95), treating MRI-based segmentations as the ground truth. Brain
regions that failed to meet performance criteria were excluded based on
automated quality control (QC) scores. Dice scores indicate a median overlap of
0.76 (IQR: 0.65-0.83). The median HD95 is 2.95 mm (IQR: 1.73-5.39). QC score
based thresholding improves median dice by 0.1 and median HD95 by 0.05mm.
Morphological differences related to sex and age, as detected by MRI, were also
replicated with CT, with an approximate 17% difference between the CT and MRI
results for sex and 10% difference between the results for age. SynthSeg can be
utilized for CT-based automatic brain segmentation, but only in applications
where precision is not essential. CT performance is lower than MRI based on the
integrated QC scores, but low-quality segmentations can be excluded with
QC-based thresholding. Additionally, performing CT-based neuroanatomical
studies is encouraged, as the results show correlations in sex- and age-based
analyses similar to those found with MRI.
