MRI brain analysis testbed (BAT) : methodology and automatic validation pipeline
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is extensively used in brain imaging research and clinical diagnostics. Increasingly, automated image processing algorithms are used for identification of tissue types within the image, such as gray matter, white matter and cerebro-spinal fluid. There is a wide range of algorithms, which vary in speed and accuracy, and it is often difficult to compare their performance in any objective and controlled fashion. The goal of this research was to design an automatic, generic, standard, extensible pipeline for objective and quantitative validation of MRI tissue classification algorithms and their processing pipelines. The main issues and requirements, for objective validation of different algorithms, are the use of common terminology, methodology, standard validation data sets, corresponding ground truth, validation metrics and statistical foundation. Based on those requirements, an automatic Brain Analysis Testbed (BAT) was developed to determine an objective evaluation score for MRI processing method. BAT supports Montreal Neurological Institute on-site or off-site processing of MRI data, accessible by a web interface (http://www.bic.mni.mcgill.ca/validation/). Validation results are stored in the BAT database permanently, allowing the comparison of newly developed processing methods with existing ones. Furthermore, BAT can be used to determine the optimal classification parameters, or the best classifier algorithm for a specific MRI classification purpose, simply by searching the BAT database. The main purposes and principles of BAT are demonstrated with some practical MRI processing examples.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".