用新型夹心U-Net自动勾画脊椎X光片，精度提升4.1%。
Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture
- 设计夹心U-Net结构，融合双激活函数提升分割能力
- 在胸部椎体分割上实现Dice分数提升4.1%
- 适合需精准脊椎轮廓的临床诊断与手术规划
脊柱活动度疾病中，精确提取和勾画椎体对评估活动障碍及屈伸运动中的变化至关重要。传统方法依赖放射科医生或外科医生手动操作，费时费力且易出错。尤其在活动度分析中，需逐个勾画每个椎体，过程繁琐且结果不一致。自动化方法可高效实现椎体识别、分割与轮廓提取，显著提高准确率并缩短时间。本研究提出一种新型U-Net变体，用于从正位X射线图像中准确分割胸椎。该方法采用“夹心”U-Net结构，结合双激活函数，在基准U-Net基础上实现Dice分数提升4.1%，显著增强分割精度并确保可靠椎体轮廓提取。
原文摘要 · Abstract (English)
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.
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