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.
Abstract (translated)
在脊柱椎体活动性疾病中，准确提取和描绘椎骨对于评估运动障碍以及监测屈伸运动期间的变化至关重要。精确的椎骨轮廓绘制在手术规划中起着关键作用；然而，这一过程传统上是由放射科医生或外科医生手动完成的，这使得该过程既费力又耗时，并且容易出现人为错误。特别是，活动性疾病分析需要对每个椎骨进行单独描绘，这是一个既繁琐又易出错的过程。 自动化方法提供了一个更高效的替代方案，能够以更高的精度和减少的时间消耗来识别、分割并描绘椎骨。在这项研究中，我们提出了一种新型的U-Net变体，旨在从X光片上精确地对胸椎进行分割（采用前后位视角）。我们的方法引入了带有双重激活函数的“三明治”结构U-Net模型，在Dice分数指标上相比基础U-Net模型提高了4.1%，从而增强了分割精度并确保了可靠的椎骨轮廓提取。
URL
https://arxiv.org/abs/2507.09158
