Multi-view parallel vertebra segmentation and identification on computed tomography (CT) images
Bibliographic record
Abstract
Vertebra segmentation and identification is the crucial step for automatic spine analysis. Manual or semi-automatic segmentation and identification is a cumbersome approach used conventionally. This paper proposes an automatic method for accurate pixel-level labeling of vertebrae on CT images. The algorithm consists of two main steps: in the first step, a pixel-link convolutional neural network is trained to generate a binary mask for the vertebral column; and in the second step, a multi-label dilated residual network identifies the labels for each vertebra. The proposed model is evaluated on the VerSe-dataset which contains 374 CT scans. This includes scans with a variety of field-of-views and healthy/disease cases acquired from multiple scanners. The model is trained and evaluated on 2D coronal and sagittal slices extracted from the CT volume. Average dice scores of 0.89 and 0.90 were achieved on two test sets released as public and hidden test sets for VerSe-dataset. The mean pixel accuracy of the predicted segmentation maps for vertebra regions are 0.72–0.86 and 0.68–0.85 for test set 1 and test 2, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".