Clinical evaluation of scoliosis primarily relies on the Cobb angle measured on standing whole-spine radiographs. However, manual measurement is affected by intra- and inter-observer variability caused by differences in end-vertebra selection, endplate definition, and vertebral boundary interpretation. In addition, low radiographic contrast and anatomical overlap can hinder accurate identification of the spinal contour. In clinical screening, rapid three-level severity classification with reduced false negatives serves as a complementary function to precise quantitative measurement, supporting case triage and missed-detection prevention. This study proposes a boundary-enhanced YOLO-based instance segmentation framework—where ‘boundary-enhanced’ refers to the reinforcement of spinal contour boundary representation through the DeepLabV3+-based segmentation head—for three-level scoliosis severity screening using clinician-assigned severity labels derived from Cobb angle measurements. Unlike semantic segmentation, which may cause class fragmentation within a single spine, the proposed method defines the entire spine as one anatomical instance and predicts a single severity label based on the global contour structure. Class-balanced offline augmentation, background-only negative samples, attention modules, and segmentation heads were comparatively evaluated. Results showed that background-only negative samples reduced false negatives, and CBAM improved accuracy while maintaining a practical model size and near-real-time inference speed under the tested environment. DeepLabV3+ provided the most stable contour reconstruction. The final model improved both contour extraction and three-level severity screening performance, suggesting that the proposed framework may be potentially useful for assisting scoliosis screening. However, further external validation and prospective evaluation are required before clinical deployment.
Hwang et al. (Mon,) studied this question.