Semiautomatic classification of intervertebral disc degeneration in magnetic resonance images of the spine
Bibliographic record
Abstract
This article describes the development of a quantitative method for computer-aided diagnosis (CAD) of intervertebral disc degeneration according to Pfirrmann's scale, a semiquantitative scale with five degrees of degeneration, in T2-weighted magnetic resonance images of the lumbar spine. The dataset consists of images of 210 discs obtained from 42 healthy individuals. The intervertebral discs were assigned Pfirrmann's grades based on independent and blind classification. Binary masks of manually segmented discs were used to compute the centroids of the regions, estimate the curvature of the spine by polynomial fitting, normalize intensities, and extract regions of interest. Texture analysis was performed using Haralick's features and moments were computed for each disc. Classification was performed using an artificial neural network using the full vectors of attributes as well as a reduced set obtained using gradient ascent search. An average true-positive rate of 75.2% and an average area under the receiver operating characteristic curve of 0.78 indicate potential application of this technique for CAD of spinal pathology.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".