Trained nnU-Net model for semantic segmentation of human adult cervical vertebrae from CT-Scans
Bibliographic record
Abstract
Automatic segmentation of the mid to low cervical spine often shows poor performance, which is detrimental to the development of patient-specific models for numerical simulations. We hypothesised that training a semantic segmentation model specifically on the cervical spine, rather than the full spine as it is usually done, would lead to better results. We trained and validated two models (nnU-Net v.2 convolutional neural network) on 172 computed tomography (CT) images: one segmenting only the cervical spine, and one segmenting the full spine. These models were then tested on an independent set of 268 CT images, unrelated to those selected for model training and internal validation. The DICE metric of the cervical model was 0.951 ± 0.051 and its 95<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile Hausdorff’s distance (H95) was 1.43 ± 1.44 mm on the test dataset. Both models presented similar performance results (p > 0.05), except for the H95 metric on the test set where the cervical model performed better (p = 0.037). Both models performed better and more homogeneously across vertebral levels than those of the literature. These results might be attributed to a better balance in the number of vertebrae per vertebral levels used for training in both models, rather than a specialisation in segmenting only a specific spine segment.Clinical Relevance— Results further highlight the importance of class balancing in semantic segmentation. The proposed model can be used to develop patient-specific models for numerical simulations, useful for both the fundamental studies of spine biomechanics and for surgery planning. Semantic segmentation of the cervical spine could also eventually assist with medical images interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".