Enhanced Spine Segmentation in Scoliosis X-ray Images via U-Net
Bibliographic record
Abstract
Scoliosis prevalence is witnessing an upward trend, rendering image segmentation an invaluable tool in appraising the condition's severity.The segmentation of spinal images, however, poses notable challenges primarily due to the image quality and the complexity of discerning the Region of Interest (ROI) on X-ray imagery.This difficulty arises from the uniform texture and luminosity of the background, complicating the ROI detection process.Our study investigates the performance of U-Net in image segmentation using anteriorposterior X-ray imagery of spines afflicted with scoliosis.A corpus of 609 high-resolution images was assembled for this purpose, partitioned into 481 training and 128 testing images.Prior to model implementation, a data augmentation process was carried out to bolster the training datasets, mitigating the risk of model overfitting.The augmentation involved mirroring and adding black and white intensity to each image, thereby generating thirteen new images from each original image.This process amplified the size of the training dataset from 481 to 6734 images.Our findings validate the efficacy of the U-Net model in accurately segmenting the spine in X-ray images, demonstrating an accuracy of 97% in training and 94% in validation, with a corresponding loss of 0.063 and validation loss of 0.16.The resultant segmentation is poised to enhance the precision of scoliosis severity assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".