The incidence of spinal diseases is rising and affecting younger people, making early and accurate diagnosis based on medical imaging crucial for treatment. However, traditional manual segmentation and measurement suffer from issues such as judgment discrepancies, time-consuming processes, and subjective errors. Existing deep learning segmentation methods still face challenges such as insufficient adaptability to complex pathological interference, high deployment barriers, and weak integration with clinical needs. This study designs an end-to-end deep learning model, with three core optimization modules: a customized U-ResNet backbone network that balances feature extraction depth and computational efficiency through multi-scale feature fusion strategies to adapt to structural differences in different spinal segments; a shape-aware attention module that integrates semantic features and contour prior features to enhance the ability to capture changes in spinal morphology and suppress background interference; and a dynamically weighted combined loss function that adjusts the weights of region and boundary losses based on vertebral body and intervertebral disc characteristics, integrating clinical constraints to meet quantitative diagnostic needs. Experiments were conducted on the Lumbar Spine MRI and VerSe datasets, and the results show that the model outperforms existing mainstream models in classification, segmentation, and intervertebral disc degeneration grading tasks. This study can provide a reference for the design of task-oriented models for medical image segmentation, and can also provide technical support for the hierarchical diagnosis and treatment of spinal diseases in primary hospitals, helping to improve diagnostic efficiency and accuracy and reduce the medical burden.
Zhao et al. (Fri,) studied this question.