A Deep Learning-Based Fully Automated Vertebra Segmentation and Labeling Workflow

Aims/Background Spinal disorders, such as herniated discs and scoliosis, are highly prevalent conditions with rising incidence in the aging global population. Accurate analysis of spinal anatomical structures is a critical prerequisite for achieving high-precision positioning with surgical navigation robots. However, traditional manual segmentation methods are limited by issues such as low efficiency and poor consistency. This work aims to develop a fully automated deep learning-based vertebral segmentation and labeling workflow to provide efficient and accurate preoperative analysis support for spine surgery navigation robots. Methods In the localization stage, the You Only Look Once version 7 (YOLOv7) network was utilized to predict the bounding boxes of individual vertebrae on computed tomography (CT) sagittal slices, transforming the 3D localization problem into a 2D one. Subsequently, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm was employed to aggregate the 2D detection results into 3D vertebral centers. This approach significantly reduces inference time and enhances localization accuracy. In the segmentation stage, a 3D U-Net model integrated with an attention mechanism was trained using the region of interest (ROI) based on the vertebral center as input, effectively extracting the 3D structural features of vertebrae to achieve precise segmentation. In the labeling stage, a vertebra labeling network was trained by combining deep learning architectures-ResNet and Transformer, which are capable of extracting rich intervertebral features, to obtain the final labeling results through post-processing based on positional logic analysis. To verify the effectiveness of this workflow, experiments were conducted on a dataset comprising 106 spinal CT datasets sourced from various devices, covering a wide range of clinical scenarios. Results The results demonstrate that the method performed excellently in the three key tasks of localization, segmentation, and labeling, with a Mean Localization Error (MLE) of 1.42 mm. The segmentation accuracy metrics included a Dice Similarity Coefficient (DSC) of 0.968 ± 0.014, Intersection over Union (IoU) of 0.879 ± 0.018, Pixel Accuracy (PA) of 0.988 ± 0.005, mean symmetric distance (MSD) of 1.09 ± 0.19 mm, and Hausdorff Distance (HD) of 5.42 ± 2.05 mm. The degree of classification accuracy reached up to 94.36%. Conclusion These quantitative assessments and visualizations confirm the effectiveness of our method (vertebra localization, vertebra segmentation and vertebra labeling), indicating its potential for deployment in spinal surgery navigation robots to provide accurate and efficient preoperative analysis and navigation support for spinal surgeries.