Model-based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets
Bibliographic record
Abstract
Abstract The cerebral stroke is a major cause for death and disability. Clinical diagnosis, therapy, and research of stroke can considerably benefit from modern image acquisition methods, which enable a detailed analysis of cerebral blood vessel anatomy as well as an examination of macrovascular and tissue blood flow dynamics. However, visual screening of these datasets can be complex and time-consuming due to the vast amount of data. This article provides an overview of a dissertation, which addresses the problem of an automatic combined analysis and visualization of high-resolution 3D and spatiotemporal (4D) image sequences from the same patient to support diagnosis, treatment decision, and research of cerebrovascular diseases. Therefore, automatic methods for the cerebrovascular segmentation, analysis of the cerebral blood flow and tissue perfusion, as well as the combined quantitative analysis and visualization of the vessel morphology and blood flow dynamics were developed. Apart from a potential clinical application, the developed methods have already proven useful in multiple clinical research studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".