Multi-Contrast MR for Enhanced Bone Imaging and Segmentation
Bibliographic record
Abstract
Musculoskeletal applications of MRI are increasing rapidly but a major challenge for researchers is the ability to efficiently and accurately segment structures of interest, such as bone, which is typically required to perform further quantitative analyses. Manual tracing is extremely time consuming and introduces problematic user variability. Automated segmentation is usually preferred; however, the accuracy and robustness of current methods still suffer from significant limitations. In this paper, we propose a novel approach for simplifying such segmentation tasks by optimizing MR protocols specifically for bone data acquisition. We present multi-contrast MR bone data acquired using short-TR T1W and fat suppression scans and demonstrate how this data can be used within an automated segmentation framework in order to improve accuracy of bone segmentation. Validation was performed on knee joint data with quantitative segmentation results on our multi-contrast data showing superior performance compared to results obtained using conventional single-contrast data. Improvements in contrast to noise ratio of 39.24 and in sensitivity and specificity of 4.09% and 4.17%, respectively, for the tibia, and 4.4% and 5.74% for the femur, were achieved.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".