Background: Parkinson’s disease (PD) is a neurodegenerative disorder with an unexplored link to the cerebellum. In the pathophysiology of balance disorders in PD, the role of the flocculonodular lobe (FL) is linked to the impairment of the dopaminergic system. Dopamine deficiency can also lead to changes in cerebellum functions, disrupting balance control. This study compares cerebellar and FL volumes between healthy controls (HC) and PD patients, analyzing their correlation with clinical outcomes. Methods: We used magnetic resonance images of 23 PD patients (14 male, 9 female) and 24 HC (9 male, 15 female). Intracranial (ICV), total cerebellar, FL, and cerebellar gray matter volumes were measured using VolBrain. Clinical outcomes in PD patients were assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS-III) to evaluate motor function, with scores correlated to volumetric data. Results: The cerebellar and gray matter volumes in HC were 115.53 ± 10.44 cm³ and 84.83 ± 7.76 cm³, respectively, compared to 126.83 ± 13.47 cm³ and 92.37 ± 9.45 cm³ in PD patients, indicating significantly larger volumes in PD patients (p < 0.05). The flocculonodular lobe gray matter volume was 1.14 ± 0.19 cm³ in PD patients and 1.02 ± 0.13 cm³ in HC, but there was a significant increase in gray matter volume in PD patients between the groups (p < 0.05). In PD patients, significant negative correlations were observed between FL volume and the UPDRS-III scores (r = − 0.467, p = 0.033) and between UPDRS-III scores and both total (r = − 0.453, p = 0.039) and normalized (r = − 0.468, p = 0.032) gray matter volumes of the FL. Conclusion: Although total gray matter volumes were larger in PD patients, the volumes of FL did not differ between groups. In Parkinson’s disease, increased cerebellar volume may regulate fine motor movements rather than balance.

Background/Objectives: To evaluate whether semi-automatic and automatic CT-based liver segmentation methods can provide clinically acceptable volumetric agreement compared with manual segmentation while improving processing efficiency in routine practice. Methods: CT images from 86 individuals were retrospectively analyzed. Liver volumes were calculated using manual segmentation, RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator. Differences among methods were assessed using repeated-measures ANOVA. Agreement with manual segmentation was evaluated using a Bland–Altman analysis, while the Dice Similarity Coefficient (DICE) and Hausdorff Distance (HD) quantified spatial overlap and boundary deviation, respectively. Processing times were recorded. Results: Mean liver volumes were 1503.9 ± 356.0 cm³ (manual), 1512.6 ± 373.6 cm³ (RVX Semi-Automatic), 1549.8 ± 367.9 cm³ (RVX Deep Learning), and 1518.3 ± 365.8 cm³ (TotalSegmentator). RVX Deep Learning produced significantly higher volumes compared with manual segmentation (p < 0.001), whereas RVX Semi-Automatic and TotalSegmentator showed no significant differences (p > 0.05). DICE values were 0.911 ± 0.032, 0.946 ± 0.018, and 0.938 ± 0.021 for RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator, respectively. HD values were highest for the deep learning-based method. Processing times were shortest for RVX Deep Learning and longest for manual segmentation. Conclusions: Semi-automatic and automatic liver segmentation methods substantially reduce processing time while maintaining clinically acceptable volumetric agreement. Among the evaluated approaches, TotalSegmentator showed the closest agreement with manual segmentation, supporting its use in routine CT-based liver volumetry. Deep learning-based segmentation, although faster, tended to overestimate volume, potentially limiting its use in applications requiring high volumetric precision.