MeTiS: A Modular Pipeline for Extracting 3D-Printable Brain-Surface Models from Conventional and Ultra-High Field MRI
Bibliographic record
Abstract
Aim: To develop a modular software pipeline for robustly extracting 3D brain-surface models from MRIs for visualization or printing. No other end-to-end pipeline specialized for neuroimaging does this directly with an interchangeable combination of methods. Materials & methods: A software application was developed to dynamically generate Nipype workflows using interfaces from the Analysis of Functional NeuroImages, Advanced Normalization Tools, FreeSurfer, BrainSuite, Nighres and the FMRIB Software Library suites. The application was deployed for public use via the LONI pipeline environment. Results: In a small, head-to-head comparison test, a pipeline using FreeSurfer for both the skull stripping and cortical-mesh extraction stages earned the highest subjective quality scores. Conclusion: We have deployed a publicly available and modular software tool for extracting 3D models from brain MRIs to use in medical education.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
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".