Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture

2025-07-12

AI Summary

Manual delineation of vertebral contours in X-ray images for spinal motion disorders is time-consuming, subjective, and poorly reproducible. To address this, we propose an enhanced U-Net architecture specifically designed for thoracic vertebrae segmentation. Our method introduces a novel “sandwich” network structure incorporating a dual-activation mechanism to strengthen boundary feature representation. It further integrates domain-specific medical image preprocessing, targeted data augmentation, and Dice loss optimization to improve segmentation robustness in low-contrast X-ray imagery. Evaluated on a thoracic vertebrae dataset, the proposed method achieves a 4.1% absolute improvement in Dice score over the standard U-Net, significantly enhancing both contour extraction accuracy and automation capability. This advancement provides a reliable technical foundation for quantitative assessment of spinal kinematics and preoperative surgical planning.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In spinal vertebral mobility disease, accurately extracting and contouring vertebrae is essential for assessing mobility impairments and monitoring variations during flexion-extension movements. Precise vertebral contouring plays a crucial role in surgical planning; however, this process is traditionally performed manually by radiologists or surgeons, making it labour-intensive, time-consuming, and prone to human error. In particular, mobility disease analysis requires the individual contouring of each vertebra, which is both tedious and susceptible to inconsistencies. Automated methods provide a more efficient alternative, enabling vertebra identification, segmentation, and contouring with greater accuracy and reduced time consumption. In this study, we propose a novel U-Net variation designed to accurately segment thoracic vertebrae from anteroposterior view on X-Ray images. Our proposed approach, incorporating a ``sandwich" U-Net structure with dual activation functions, achieves a 4.1% improvement in Dice score compared to the baseline U-Net model, enhancing segmentation accuracy while ensuring reliable vertebral contour extraction.

Problem

• Automating spinal vertebrae contouring for mobility disease analysis
• Reducing manual labor and errors in vertebral segmentation
• Improving accuracy in X-Ray vertebrae segmentation using U-Net

Innovation

• Novel Sandwich U-Net architecture
• Dual activation functions integration
• Improved Dice score by 4.1%