Efficient SpineUNetX for X-ray: A spine segmentation network based on ConvNeXt and UNet

Abstract

Accurate localization and delineation of vertebrae are crucial for diagnosing and treating spinal disorders. To achieve this, we propose an efficient X-ray full-spine vertebra instance segmentation method based on an enhanced U-Net architecture. Several key improvements have been made: the ConvNeXt encoder is employed to effectively capture complex features, and IFE feature extraction is introduced in the skip connections to focus on texture-rich and edge-clear clues. The CBAM attention mechanism is used in the bottleneck to integrate coarse and fine-grained semantic information. The decoder employs a residual structure combined with skip connections to achieve multi-scale contextual information and feature fusion. Our method has been validated through experiments on anterior-posterior and lateral spinal segmentation, demonstrating robust feature extraction and precise semantic segmentation capabilities. It effectively handles various spinal disorders, including scoliosis, vertebral wedging, lumbar spondylolisthesis and spondylolysis. This segmentation foundation enables rapid calibration of vertebral parameters and the computation of relevant metrics, providing valuable references and guidance for advancements in medical imaging.