Sensitivity of MRI signal distribution within the intervertebral disc to image segmentation and data normalisation
Bibliographic record
Abstract
There is a lack of early biomarkers of intervertebral disc (IVD) degeneration. Thus, the authors developed the analysis of magnetic resonance signal intensity distribution (AMRSID) method to analyse the 3D distribution of the T2-weighted MR signal intensity within the IVD using normalised histograms, weighted centres and volume ratios. The objective was to assess the sensitivity of the AMRSID method to the segmentation process and data normalisation. Repetition of the semi-automatic segmentation by the same operator did not influence the quality of the contour or our new MR distribution parameters whereas the skills of the operator influenced only the MR distribution parameters, and the instructions given prior to the segmentation influenced both the quality of the contour and the MR distribution parameters. Bone normalisation produces an index that jointly highlights IVD and bone health, whereas cerebrospinal fluid normalisation only suppresses the effect of the acquisition gain. This robust AMRSID method has the potential to improve the diagnostic with earlier biomarkers and the prognosis of evolution.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".