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 95thpercentile 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".