fMROI: a simple and adaptable toolbox for easy region-of-interest creation
Bibliographic record
Abstract
ABSTRACT This study introduces fMROI, an open-source software designed for creating regions-of-interest (ROIs) and visualizing magnetic resonance imaging data. fMROI offers a user-friendly graphical interface that simplifies the creation of complex ROIs. It is compatible with various operating systems and enables the integration of user-specified algorithms. Comparative analysis against popular neuroimaging software demonstrates the feasibility, applicability, and ease of use of fMROI. Notably, fMROI’s interactive graphical interface with a real-time viewer allows users to identify inconsistencies and design more accurate ROIs, saving significant time by avoiding errors before storing ROIs as NIfTI files. Additionally, fMROI supports automation through command-line accessibility, making it ideal for large-scale analyses. As an open-source platform, fMROI provides a valuable resource for researchers in the neuroimaging community, facilitating efficient ROI creation and streamlining neuroimage analysis.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".