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:this https URL
View on arXiv@article{kondrateva2025_2504.15931,
title={ Benchmarking the Reproducibility of Brain MRI Segmentation Across Scanners and Time },
author={ Ekaterina Kondrateva and Sandzhi Barg and Mikhail Vasiliev },
journal={arXiv preprint arXiv:2504.15931},
year={ 2025 }
}