TotalSegmentator MRI is an open-source, user-friendly tool that provides automatic, robust sequence-independent segmentations of major anatomic structures on MRI scans; the ready-to-use online tool is available at https://totalsegmentator.com .

Purpose In idiopathic normal pressure hydrocephalus (iNPH), longitudinal monitoring of ventricular size is important for diagnosis and treatment follow-up. This study aimed to validate a fully automated AI model for CT ventricular volumetry with subcompartments and to compare AI-derived volume changes with routine radiology assessments. Methods This retrospective, single-center study included 88 patients with iNPH and 456 non-contrast-enhanced head CT examinations. The model was trained on 38 manually labeled CT scans with 12 ventricular subcompartments. Outcomes included segmentation accuracy, correspondence between AI-derived longitudinal ventricular volume changes and radiology report categories (decreased, unchanged, increased), radiologist detection thresholds for ventricular change, and paired pre- and postoperative volume changes in 22 patients with ventriculoperitoneal shunt. Results Mean segmentation accuracy was high (Dice, 0.83). 91% of 100 segmentations were rated as excellent by an expert neuroradiologist. AI-derived ventricular volume changes corresponded well to radiology report categories (median total ventricular volume changes of -17% in cases reported as decreased, 0% in unchanged cases, and +22% in increased cases; all p < 0.001). Radiologists reported ventricular volume change in 50% of cases at an AI-measured relative volume change of +/-6%, and in 90% of cases at +21% for enlargement and -18% for decrease. After shunt placement, ventricular volume decreased by -8% (median), with the largest relative reductions observed in the right temporal and occipital horns. Conclusions Automated AI-based ventricular segmentation on CT enables accurate and reproducible assessment of ventricular volume changes in iNPH and complements routine radiological evaluation for longitudinal and postoperative monitoring.

Background: Age- and sex-specific CT reference percentile curves of ventricular volume may support objective assessment of ventricular enlargement. We aimed to generate such curves by combining a locally deployed large language model (LLM) for cohort curation with nnU-Net 12-subcompartment segmentation. Methods: This retrospective study did not require formal ethics approval under the Swiss Human Research Act. We screened 42,211 head CT reports from January 2019 to April 2024. A phrase prefilter identified 9622 candidates for German-tuned 70-billion-parameter LLM review of stroke, leukoencephalopathy, parenchymal defects, enlarged ventricles, and space-occupying lesions; 6516 remained. A second LLM workflow screened 210,761 clinical data warehouse summaries to exclude dementia, organic brain disease, or cognitive decline. After image-availability and series-mismatch exclusions, the final cohort comprised 2608 CTs from 2608 patients (1374 males, 1234 females; age 14–98 years). Ventricular masks were obtained using a validated nnU-Net tool integrated in TotalSegmentator. Results: LLM labelling accuracy in 471 blinded reports ranged from 0.966 to 0.992. CDW screening audit showed 91% agreement, sensitivity 0.96, and negative predictive value 0.96. Median total ventricular volume rose from 10.94 mL in young females and 13.42 mL in young males to 21.97 mL and 31.97 mL, respectively, in the 60–69 age group. Except for the fourth ventricle, compartments showed positive age correlations (r = 0.44–0.69); the fourth ventricle showed no relevant association (r = 0.01 males; r = 0.05 females). After adjustment for age, male sex remained associated with greater total ventricular volume (6.54 mL; 95% CI, 5.61–7.46; $p<0.001$). Conclusions: This study provides age- and sex-specific CT ventricular nomograms in a selected reference cohort. The LLM-assisted curation and nnU-Net segmentation workflow provides a feasible approach for large-scale cohort analysis.

(1) Background: Hydrocephalus poses challenges in pediatric neuroimaging, and conventional MRI methods have limitations regarding its accurate quantification. Synthetic MRI (SyMRI) offers a promising automated solution to assess intracranial compartment volumes. However, its clinical utility in pediatric patients remains underexplored. Our study aims to assess the accuracy and reliability of automated CSF volume measurements using SyMRI in children and adolescents, comparing them with manual measurements and human expert ratings. (2) Methods: A single-center retrospective study included 124 pediatric patients undergoing cranial MRI with SyMRI. CSF, brain parenchyma, and intracranial volumes were measured using both automated SyMRI and manual methods. Human radiologists assessed hydrocephalus subjectively. (3) Results: Correlations between manual and SyMRI volume evaluations were significant. Human raters demonstrated good agreement on hydrocephalus ratings among themselves (Fleiss’ kappa = 0.66, p < 0.001) but only moderate agreement with the SyMRI method (Cohen’s kappa = 0.45, p < 0.001). SyMRI volumes were systematically tendentially higher in SyMRI (CSF p = 0.005; BPV and ICV p < 0.001). (4) Conclusions: Our findings highlight SyMRI’s reliability in assessing hydrocephalus and intracranial volumes in pediatric cases. Despite some differences from manual measurements, the strong correlation suggests its clinical viability.