Preoperative MRI Whole-Vertebrae Segmentation in Patients with Severe Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
Adolescent Idiopathic Scoliosis (AIS) is a complex 3D deformity of the spine that requires surgical treatment for the most severe cases. Magnetic resonance imaging (MRI) of the spine is used for surgical planning and provides a reliable 3D pre-operative model to which intra-operative images can be registered for surgery assistance. In this work, we propose a novel approach for segmenting whole vertebrae in MRI of patients with severe scoliosis. It consists in a Unet segmentation model trained on MRIs of AIS patients that is further refined through a marker-controlled watershed algorithm. The external markers are obtained by aligning a template vertebra onto the initial segmentation. Results on 50 MRI volumes of preoperative AIS patients demonstrate an overall Dice score of 84%, but most importantly, an improvement of 47% at the level of the vertebral arch attributed to the refinement step. While most previous works have focused on segmenting the vertebral body, this work improves the segmentation of the vertebral processes, which are more challenging due to their irregular shape.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".