Modality-Independent nnU-Net-Based Segmentation of Psoas Muscle and Vertebral Bodies in CT and MRI for Spinal Metastases Patients Undergoing SBRT
Bibliographic record
Abstract
Musculoskeletal (MSK) imaging biomarkers may support risk stratification and longitudinal monitoring in spinal cancer patients undergoing stereotactic body radiotherapy (SBRT), particularly those at risk of osteosarcopenia—a condition involving concurrent loss of bone and muscle mass. While CT-based biomarkers are well established, MRI provides superior soft-tissue contrast without ionizing radiation, making it an attractive modality for longitudinal assessments. In this work, we leverage aligned CT–MRI pairs as a weakly supervised framework to train nnU-Net models for automatic segmentation of the psoas muscle and vertebral bodies in MRI. CT-derived segmentations were propagated to MRI volumes, enabling model training without manual MRI annotations. Using 5-fold crossvalidation and nnU-Net postprocessing, the combined psoas model achieved a mean Dice score of 0.90, and the best vertebral body model achieved 0.82 for L1–L5. These results support the feasibility of radiation-free MSK biomarker extraction from MRI in SBRT populations.
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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".