Bibliographic record
Abstract
This study aimed to create a comprehensive statistical atlas of cerebral arteries to accurately capture variations among individuals and across different age groups. We utilized 1,336 publicly available multicenter magnetic resonance angiography (MRA) and T1-weighted MRI datasets, employing an automated blood vessel segmentation method, FFCM-MRF, to segment all blood vessels and measure their radii. Subsequently, the binary segmentation and vascular radius images were nonlinearly registered to the Montreal Neurological Institute (MNI) brain template using the T1-weighted MRI dataset. This process resulted in the creation of atlases that illustrate the probability of arterial occurrence, the average arterial radius, and the standard deviation of the arterial radius. The constructed vascular statistical atlas effectively showcases the major arteries and, when integrated with the probability atlas and the average vessel radius atlas, indicates a significantly higher probability of larger arteries, which decreases as the vessel radius diminishes. This observation aligns with previous research findings, and the similarity between the probability atlas and individual vascular images reached as high as 0.9659. In conclusion, this atlas effectively covers arterial radius information across nearly the entire age range, enabling the identification of variations between individual arterial voxel radii and the population using this atlas, thereby providing an important reference for cerebral vascular research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".