Microstructure-informed brain tissue classification using clustering of quantitative MRI measures
Bibliographic record
Abstract
Traditional tissue classification approaches in vivo use voxel intensities from conventional clinical magnetic resonance (MR) images for segmentation, which does not incorporate information about specific aspects of microstructure. With the Clustering for Anatomical Quantification and Evaluation (CAQE) framework, quantitative MRI measures can be used to classify tissue based only on microstructural features with no spatial enforcement, and pathological changes in disease can be evaluated. In this study, maps of whole-brain myelin water fraction, microscopic fractional anisotropy, and tissue heterogeneity were used to classify brain tissue in 25 healthy participants. CAQE was then applied to 25 participants with multiple sclerosis (MS), where tissue classifications indicated areas of increased demyelination and axonal injury in white matter compared with a healthy average tissue classification. Severity scores were derived from tissue classifications to quantify diffuse white matter damage, and correlated significantly with cognitive ability in MS. The CAQE framework can be adapted for other applications and extended to use different quantitative MRI measures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".