Towards Reliable Measurement of Cerebellar Morphology : A comparative assessment of segmentation pipelines

Background. Characterizing cerebellar morphology is fundamental for accurately mapping its structure and function across individuals, yet remains challenging due to its densely foliated architecture. Although multiple automated segmentation pipelines exist, the measurement reproducibility of these tools has not been benchmarked. Methods. We conducted a systematic assessment of robustness for cerebellar morphology estimates using four commonly used pipelines: one classic parcellation method (CERES), two deep-learning methods (ACAPULCO, DeepCERES), and one voxel-based morphometry toolbox (SUIT). Leveraging the HNU test-retest dataset, which provides MRI scans for ten timepoints per individual over a month, we evaluated the test-retest reliability for each of four pipelines using ReX, an integrative tool for quantifying and optimizing measurement reliability and individual differences. We quantified intra- and inter-individual variability, as well as the Intraclass Correlation Coefficient (ICC), for each pipeline at both global and region-of-interest levels. Results. Overall, all pipelines yielded highly reliable segmentation volumes (ICC > 0.8). Across pipelines, DeepCERES demonstrated the strongest performance, exhibiting high inter-individual consistency and low intra-individual variability. Importantly, our analysis highlighted substantial heterogeneity in reliability across lobules for each method. Lobule X consistently showed reduced reliability whereas lobules I-V were reliably estimated across all pipelines. Conclusion. Our work evaluated the robustness of cerebellar segmentation pipelines. While DeepCERES offers a robust global performance, substantial lobule-specific variability underscores the need for reliability-aware pipeline selection to optimize morphology estimation in research.