Brain extraction using geodesic active contours
Bibliographic record
Abstract
Extracting the brain cortex from magnetic resonance imaging (MRI) head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool (BET) produces a brain mask which may be too smooth for practical use. This paper presents a novel brain extraction tool based on three-dimensional geodesic active contours, connected component analysis and mathematical morphology. Based on user-specified intensity and contrast levels, the proposed algorithm allows an active contour to evolve naturally and extract the brain cortex. Experiments on synthetic MRI data and scanned coronal and axial MRI image volumes indicate successful extraction of tight perimeters surrounding the brain cortex. Quantitative evaluations on both synthetic phantoms and manually labeled data resulted in better accuracy than BET in terms of true and false voxel assignment. Based on these results, we illustrate that our brain extraction tool is a robust and accurate approach for the challenging task of automatically extracting the brain cortex in MRI data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".