A Deep-Learning-Based Lumbosacral Localization and Landmark Detection Network for Automatic Lumbar Stability and Spondylolisthesis Grading Assessment

The accurate detection of vertebral landmarks is crucial for clinical diagnosis and research on the lumbar stability and spondylolisthesis grading. However, the small size of vertebral landmarks in X-ray images and the morphological similarity among vertebrae complicate this detection task. Recent advances in deep learning have enhanced the spinal landmark detection. To further improve the assessment methods for the lumbar stability and spondylolisthesis grading, we propose the LSLD-Net, a novel network based on the lumbosacral localization and landmark detection. In clinical practices, the X-ray images used for evaluating the lumbar spine stability can often include the extraneous information from non-lumbosacral areas, such as the thoracic spine. The proposed LSLD-Net first extracts the lumbosacral region from the complex X-ray images and then performs the precise landmark detection within the identified area. The landmark detection stage integrates the HRNet and U-net architectures, effectively capturing the long-range contextual information, the overall structural layout, and the fine local details in lumbar X-ray images to optimize landmark detection. Additionally, we propose a Multi-Scale Attention Module that enhances the relevant features and suppresses the irrelevant ones through channel and spatial attention, thereby achieving precise landmark detection and improving the robustness of the network. The evaluations on the private and public BUU-LSPINE datasets indicate that the LSLD-Net surpasses other state-of-the-art methods in landmark detection, enhancing the accuracy and efficiency of Sagittal Displacement and Intervertebral Space Angle measurements. This performance excels in assessing the lumbar stability and spondylolisthesis grading, offering the significant support to clinicians for early quantitative diagnosis and evaluation.