对比两种脑部MRI分割方法，发现扫描仪差异导致小脑区体积波动达8%。
Benchmarking the Reproducibility of Brain MRI Segmentation Across Scanners and Time
- 用两个数据集测试FastSurfer和SynthSeg的分割稳定性
- 小脑区体积在相同条件下仍存在7-8%的波动
- 提出基于表面的质量筛选法提升结果可靠性
从结构MRI中准确、可复现地获取脑形态测量值对监测跨时间与跨成像域的神经解剖变化至关重要。尽管深度学习加速了分割流程，但扫描仪引起的变异性和可复现性限制依然存在，尤其在纵向和多中心研究中。本研究评估了两种现代分割流程FastSurfer和SynthSeg（均集成于FreeSurfer）的性能，使用两个互补数据集：一个17年纵向队列（SIMON）和一个9中心重复扫描队列（SRPBS）。通过Dice系数、Surface Dice、Hausdorff距离（HD95）和平均绝对百分比误差（MAPE）量化跨扫描分割变异。结果显示，在受控重复扫描条件下，杏仁核和腹侧间脑等小亚皮层结构体积波动高达7-8%。这引发关键问题：若形态测量噪声本身达到7-8%，是否还能检测出5-10%的细微纵向变化？研究还分析了配准模板与插值方式的影响，并提出基于表面的质量过滤策略以提升分割可靠性。该研究为脑形态测量可复现性提供了可复现基准，强调真实世界神经影像研究中需采取标准化策略。代码与图表见：https://github.com/kondratevakate/brain-mri-segmentation
原文摘要 · Abstract (English)
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
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