Combining PropSeg and a convolutional neural network for automatic spinal cord segmentation in pediatric populations and patients with spinal cord injury
Bibliographic record
Abstract
Abstract Segmentation of the spinal cord is an essential process for the accurate delineation of spinal cord structures but can be a tedious task for experts when using manual or semi‐automated tools. On the other hand, existing automatic segmentation algorithms have not been developed with the pediatric or injured spinal cord in mind. This study presents a novel automated segmentation method that combines the flexibility of deterministic approaches and the powerfulness of neural networks, applied to pediatric and injured spinal cord magnetic resonance imaging (MRI) data. The method first applies the PropSeg algorithm several times on small patches of the spinal cord MRI with various initialization parameters. Then, a convolutional neural network concatenates all these small segmentations with the original MR images to compute a final segmentation. Our results demonstrate good performances on the whole spinal cord (Dice score = 0.88 vs. 0.9) while outperforming existing methods on spinal cord injury regions (0.8 vs. 0.63).
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".