MSA-Net: A Multi-Scale Attention Network with Contrastive Learning for Robust Intervertebral Disc Labeling in MRI

Accurate labeling of intervertebral discs (IVDs) in MRI scans is crucial for diagnosing spinal-related diseases such as osteoporosis, vertebral fractures, and IVD herniation. However, automatic IVD labeling remains challenging. The main issues include visual similarity to surrounding bone, anatomical variation across individuals, and inconsistencies between MRI scans. Traditional post-detection disc labeling methods often struggle when localization algorithms miss discs or generate false positives. To address these challenges, we propose MSA-Net, a novel multi-scale attention network designed for semantic IVD labeling, emphasizing the use of prior geometric data. MSA-Net efficiently extracts multi-scale features and models intricate spatial dependencies throughout the spinal structure. We also integrate contrastive learning to enforce feature consistency. This helps the network distinguish IVDs from surrounding tissues. Extensive experiments on multi-center spine datasets demonstrate that MSA-Net consistently outperforms previous methods across MRI T1w and T2w modalities. These improvements demonstrate MSA-Net’s ability to handle variability in disc geometry, tissue contrast, and missed detections that challenge prior methods.