基于深度学习的旋转物体检测算法用于自动测量科布角在X射线影像中的脊椎病
Yichong Liu1, Zhiliang Shi1, Chaoyang Xiao1
1School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China.
Quantitative imaging in medicine and surgery
|July 3, 2025
概括
这项研究引入了一种增强的YOLOv8-DSF模型,通过改进旋转脊椎检测来更准确地评估脊椎病. 该模型显著提高了科布角测量精度,有助于更好地临床评估和管理脊柱曲.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 脊柱病涉及脊柱曲异常,在青少年发育过程中构成挑战.
- 科布角测量是评估脊柱形严重程度的标准.
- 准确的科布角测量受到检测旋转的脊椎物体的困难所阻碍.
研究的目的:
- 开发一个增强的模型,以更好地检测旋转脊椎.
- 为了提高科布角测量的准确性,用于对脊柱结的评估.
- 提供一个更可靠的工具,用于临床脊椎病评估.
主要方法:
- 使用了基于YOLOv8n导向界限框 (OBB) 框架的重新设计的YOLOv8-DSF模型.
- 集成了创新的模块,以加强对旋转物体的检测.
- 该模型在一个定制的数据集上进行了训练和验证,该数据集将私人和公共数据与增强相结合.
主要成果:
- 该模型实现了0.626的平均平均精度 (mAP50) 和0.424的mAP50-95,超过了10%以上的基线.
- 科布角的准确性被证明是对称平均绝对百分比误差 (SMAPE) 为8.43和平均绝对误差 (MAE) 为5.09.
- YOLOv8-DSF模型在Cobb角度预测的公共数据集上表现出色.
结论:
- 改进的模型在检测旋转脊椎方面显著优于基线和现有方法.
- 这一进步导致了更准确的科布角测量,用于评估脊椎病.
- 这项研究为治疗脊椎病的临床应用提供了一个有前途的工具.
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