Human Hip Joint Cartilage: MRI Quantitative Thickness and Volume Measurements Discriminating Acetabulum and Femoral Head
Bibliographic record
Abstract
This paper aims at developing a quantitative system for measuring human hip cartilage thickness and volume using magnetic resonance imaging (MRI). A new MRI-acquisition technique, named axial rotation, where the acquisition planes are organized around a virtual axis, was used. The MRI protocol consists of a 2-D multiple-echo data image combination (MEDIC) using water excitation. Inner and outer interface contours of acetabulum and femoral head cartilage are obtained using a semiautomated 3-D segmentation method and combined to form 3-D surfaces. A local spherical coordinate system computed from the original contours enables cartilage thickness and volume computation. An anatomical labeling is performed automatically for thickness and volume measurements in predefined subregions: inferior, anterior, superior, and posterior. A registration module is introduced allowing the assessment of cartilage changes over time. Validation of the system was conducted with three protocols each involving data obtained from nine subjects: 1) registration process accuracy; 2) intrareader reproducibility; and 3) intervisit coefficient of variation. Data showed excellent correlation coefficients for either the intrareader (r>or=0.0942, p<0.0001 ) or intervisit (r>or=0.0837, p<0.005) protocols. This noninvasive system, which enables the quantification of cartilage thickness and volume in the human hip joint using MRI, is the first to discriminate the acetabular and femoral head cartilage throughout the entire hip without the use of an external device, and to implement hip registration for follow-up studies on the same subject.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".