Vertebral Bone Segmentation and Detection of Non-Traumatic Vertebral Compression Fractures with CNN from Computed Tomography Images
Bibliographic record
Abstract
Non-traumatic vertebral compression fractures are increasingly common due to longer life expectancies.Age-related bone mass loss significantly contributes to these fractures.Typically asymptomatic for extended periods, early detection of non-traumatic vertebral compression fractures can reduce associated health issues and enable more effective treatment.Deep learning methods have shown high accuracy and sensitivity in detecting, classifying, diagnosing, and segmenting various pathological conditions in healthcare.Recently, these methods have been applied more frequently in the detection of non-traumatic vertebral compression fractures and vertebral body segmentation research.This study introduces a unique dataset to apply deep learning techniques, using raw computed tomography (CT) images of patients.The dataset was compiled from retrospective CT images taken at Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology.It includes 197 individuals, with 100 diagnosed with nontraumatic vertebral compression fractures and 97 without.Radiological diagnoses of nontraumatic vertebral compression fractures were added based on CT reports.The dataset comprises a total of 118,200 cross-sectional images in DICOM format, which were enhanced using the Wiener filter.The U-Net network was used to segment 6,301 vertebrae, achieving a 100% dice overlap index score.Additionally, 593 features of vertebral fractures confirmed by reports were extracted using the radiomics method, and 537 features were selected via the logarithmic lambda method.The convolutional neural network (CNN) classification model was employed, achieving an accuracy of 86.7%.The classification results were evaluated through ROC-AUC, loss, and accuracy graphs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".