segcsvd <sub>PVS</sub> : A convolutional neural network‐based tool for quantification of enlarged perivascular spaces (PVS) on T1‐weighted images
Bibliographic record
Abstract
Abstract Background Enlarged perivascular spaces (PVS) are imaging biomarkers of cerebral small vessel disease (CSVD) associated with age, hypertension, and neurodegenerative conditions. Despite their clinical relevance, accurate quantification of PVS on T1‐weighted magnetic resonance imaging (MRI) remains challenging due to both variability across imaging protocols, and their small size and limited contrast. Automated methods such as convolutional neural networks (CNNs) offer a scalable solution, but existing tools are limited in performance and generalizability. Method This study introduces segcsvd PVS , a CNN‐based tool designed for automated PVS segmentation on T1‐weighted images, based on a hierarchical framework incorporating anatomical information and robust training strategies. It was trained on semi‐automated RORPO‐based ground truth data and validated using both manual and semi‐automated segmentations. A large and comprehensive cohort ( n = 1351) spanning multiple datasets characterized by diverse imaging protocols, patient populations, and anatomical characteristics was used for training and evaluation. Performance metrics, robustness to variations in image quality, and age‐related associations with PVS burden were rigorously evaluated against established RORPO‐based methods. Result Segcsvd PVS achieved high sensitivity for basal ganglia PVS (SNS = 0.81 ± 0.13) and identified significantly larger volumes (86.1 ± 67.1 mm 3 ) compared to human tracers (47.2 ± 26.5 mm 3 , 48.6 ± 28.4 mm 3 . Our tool demonstrated strong age‐related correlations with PVS volumes across three diverse datasets (TEST: r = 0.41, CI = [0.03, 0.68]; ADNI: r = 0.38, CI = [0.30, 0.46]; CAHHM: r = 0.41, CI = [0.35, 0.46]). Although similar but weaker trends were observed for non‐basal ganglia PVS, segcsvd PVS demonstrated superior robustness to variations in contrast and noise across both regions, with minimal changes in age‐related correlations (∆r ≤ 0.08) compared to the RORPO‐based methods (∆r ≤ 0.39). Conclusion Segcsvd PVS is a reliable tool for PVS segmentation, particularly in basal ganglia regions, offering superior sensitivity, robustness to imaging variability, and enhanced detection of biologically relevant age‐related associations. These findings support its application in large‐scale studies and clinical research to advance our understanding of PVS contributions to CSVD and dementia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".