Accurate and reproducible brain morphometry from structural MRI is critical
for monitoring neuroanatomical changes across time and across imaging domains.
Although deep learning has accelerated segmentation workflows, scanner-induced
variability and reproducibility limitations remain-especially in longitudinal
and multi-site settings. In this study, we benchmark two modern segmentation
pipelines, FastSurfer and SynthSeg, both integrated into FreeSurfer, one of the
most widely adopted tools in neuroimaging.
Using two complementary datasets – a 17-year longitudinal cohort (SIMON) and
a 9-site test-retest cohort (SRPBS)-we quantify inter-scan segmentation
variability using Dice coefficient, Surface Dice, Hausdorff Distance (HD95),
and Mean Absolute Percentage Error (MAPE). Our results reveal up to 7-8% volume
variation in small subcortical structures such as the amygdala and ventral
diencephalon, even under controlled test-retest conditions. This raises a key
question: is it feasible to detect subtle longitudinal changes on the order of
5-10% in pea-sized brain regions, given the magnitude of domain-induced
morphometric noise?
We further analyze the effects of registration templates and interpolation
modes, and propose surface-based quality filtering to improve segmentation
reliability. This study provides a reproducible benchmark for morphometric
reproducibility and emphasizes the need for harmonization strategies in
real-world neuroimaging studies.
Code and figures: https://github.com/kondratevakate/brain-mri-segmentation

Este artículo explora los viajes en el tiempo y sus implicaciones.

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