Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture
This work addresses the labor-intensive and error-prone manual contouring process for radiologists and surgeons in spinal mobility disease assessment, representing an incremental improvement in automated segmentation.
The paper tackled the problem of automating spinal vertebrae contouring on X-ray images for mobility disease analysis, achieving a 4.1% improvement in Dice score over a baseline U-Net model.
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.