Automated finding of the Willis ring in MR angiography images using fuzzy knowledge base
Bibliographic record
Abstract
This paper proposes an automated method for finding the Willis ring from the human brain MR angiography (MRA) images, which can depict cerebral arteries with high contrast. It strongly helps screening of unruptured cerebral aneurysm in MRA images. The proposed method consists of (1) segmenting cerebral arteries from MRA images, (2) skeletonization of artery trees, and detection of furcations, and (3) finding furcations in the Willis ring using genetic algorithm (GA) based on fuzzy knowledge base (fuzzy KB). Fuzzy KB gives knowledge about the Willis ring that consists of arteries and furcations. GA finds a set of furcations by optimizing an objective function. The objective function used by GA estimates fitness of a set of furcations using fuzzy KB. Our method was first applied to a 3-D phantom data generated by computer simulation. The result demonstrated that our method detected all suitable furcations correctly. Next, it was applied to MRA volume data of two normal healthy volunteers. In any cases, the proposed method detected desired all furcations in the Willis ring.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".