From contrast-driven segmentation to central lumbar spinal stenosis grading: a comprehensive multi-view spinal MRI image analysis.

Abstract

Central lumbar spinal stenosis, a prevalent degenerative spinal disorder, severely impacts the quality of life for those affected. Axial and sagittal MRI images offer diverse information on tissue structure and lesions, which is crucial for accurate diagnosis. However, MRI-based diagnostic approaches still have poor lesion localization, insufficient cross-view alignment, underutilization of multi-view MRI information, and limited generalization across patient variability. To address these problems, we proposed an Encompassing Lumbar Central Spinal Stenosis Grading Model via Multi-view MRI Image Fusion called ELSG-MF. ELSG-MF consists of three stages: the first stage utilizes the extraction of robust pseudo-labels through a contrast-driven consistency reinforcement technique to guide Med-SAM in localizing and segmenting spinal tissue components. The Sagittal-Axial Pairing (SAP) Algorithm was developed by stage2 to integrate the spatial anatomical relationship between the vertebral body and the intervertebral disc, facilitating the correlation pairing between sagittal and axial images. Stage3 subsequently innovated the multi-view Adaptive Fusion (M²AF) module, which enables adaptive dynamic fusion of anatomical features across views. M²AF enhances the extraction of contextual complementary information, and significantly improves the model's capacity to detect subtle variations in the degree of narrowness. A series of studies show that our model achieves an overall accuracy of 0.8631, AUC of 0.96, and F1-score of 0.8614. These results indicate that our model substantially outperforms mainstream approaches, attaining superior segmentation and grading accuracy, exhibiting robust generalization and clinical application potential.