PCSS: Skull Stripping With Posture Correction From 3D Brain MRI for Diverse Imaging Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it