Lumbar region perception segmentation: a novel algorithm for automated vertebral motion parameter measurement.

Background: Lower back pain frequently results from irregular lumbar spine movement. Quantifying vertebral motion is crucial for diagnosing lumbar spine disorders, but the accuracy and efficiency of traditional methods are limited. To address this limitation and automate the measurement of vertebral motion parameters for clinical diagnosis, we developed the vertebra segmentation network (VerSeg-Net), a novel deep learning-based approach for segmenting lumbar vertebrae and measuring motion parameters from dynamic X-ray images. Methods: The VerSeg-Net integrates: the region-aware (RA) module, which partitions features into non-overlapping blocks and applies dynamic sparse attention to filter irrelevant regions; and adaptive receptive field feature fusion (AFF), which fuses multi-scale contextual features via deformable convolutions. The model was trained on 50 patients (using 2,000 dynamic X-ray sequences; 512×512 pixels) with lumbar disorders, using a Philips UNIQ FD20 C-arm. Its performance was benchmarked against the U-Net, ResUnet, DeeplabV3+, and PFNet model. Statistical significance was assessed via paired Results: In terms of its segmentation accuracy, VerSeg-Net had a mean dice similarity coefficient (DSC) of 96.2% ( Conclusions: VerSeg-Net is a reliable and accurate method for analyzing lumbar spine motion, and thus could significantly aid in clinical diagnosis and treatment planning.