An objective approach to digital removal of saccular aneurysms: technique and applications
Bibliographic record
Abstract
Human studies of haemodynamic factors in the pathogenesis of cerebral aneurysms require knowledge of the pre-aneurysmal vasculature. This paper presents an objective and automated technique to digitally remove an aneurysm and reconstruct the parent artery, based on lumen geometries segmented from angiographic images. Relying on robust computational geometry concepts, notably Voronoi diagrams of the digitised lumen surface, the aneurysm attachment region is first defined objectively using lumen centrelines. Centrelines within this region are replaced by smooth interpolations, which then guide the interpolation of Voronoi points within the attachment region. Combined with Voronoi points from outside the attachment region, the parent artery lumen, without the aneurysm, can be reconstructed. Plausible reconstructions were obtained, automatically, for a set of 10 side-wall or terminal aneurysms, of various sizes and shapes, from the ANEURISK project data set. Application of image-based computational fluid dynamics analysis to a five side-wall aneurysm cases data set revealed an association between the recently proposed gradient oscillatory number (GON) and the site of aneurysm formation in four of five cases; however, elevated GON was also evident at non-aneurysmal sites. A potential application to the automated delineation of aneurysms for morphological characterisations is also suggested. The proposed approach may serve as a broad platform for investigating haemodynamic and morphological factors in aneurysm initiation, rupture and therapy in a way amenable to large-scale clinical studies or routine clinical use. Nevertheless, while the parent artery reconstructions are plausible, it remains to be proven that they are faithful representations of the pre-aneurysmal artery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".