PCSS: Skull Stripping With Posture Correction From 3D Brain MRI for Diverse Imaging Environment
Bibliographic record
Abstract
A subject’s head position in magnetic resonance imaging (MRI) scanners can vary significantly with the imaging environment and disease status. This variation is known to influence the accuracy of skull stripping (SS), a method to extract the brain region from the whole head image, which is an essential initial step to attain high performance in various neuroimaging applications. However, existing SS methods have failed to accommodate this wide range of variation. To achieve accurate, consistent, and fast SS, we introduce a novel two-stage methodology that we call posture correction skull stripping (PCSS): the first involves adjusting the subject’s head angle and position, and the second involves the actual SS to generate the brain mask. PCSS also incorporates various machine learning techniques, such as a weighted loss function, adversarial training from generative adversarial networks, and ensemble methods. Thorough evaluations conducted on five publicly accessible datasets show that the PCSS method outperforms current state-of-the-art techniques in SS performance, achieving an average increase of 1.38 points on the Dice score and demonstrating the contributions of each PCSS component technique.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".