基于变压器的脊椎本地化和脊柱曲的分类
Syeda Humaira Batool1, Noshaba Liaquat1, Sajid Gul Khawaja1
1Department of Computer & Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Scientific reports
|September 29, 2025
概括
这项研究引入了一种用于脊柱分析的自动化系统,使用先进的AI模型准确地定位脊椎和细分脊柱图像. 该系统在分类脊柱形方面实现了高准确度,有助于早期诊断和治疗.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 脊柱生物力学 脊柱生物力学
背景情况:
- 脊柱疾病和形需要精确的诊断才能有效治疗.
- 准确地定位和细分脊椎对于评估脊柱健康至关重要.
- 目前的方法可能缺乏详细的脊髓分析所需的精度.
研究的目的:
- 开发一个用于脊柱分析的自动化系统,包括脊椎本地化,细分和变形分类.
- 评估深度学习模型,检测变压器 (DETR) 和SegFormer在脊柱图像分析中的性能.
- 为了比较不同机器学习分类器对脊柱形分类的准确性.
主要方法:
- 使用检测变压器 (DETR) 确定68个角点的脊椎本地化.
- 使用SegFormer进行脊柱细分,以精确划分脊柱.
- 用于形状分析和分类的脊柱中心配置文件的生成.
- 使用SVM,随机森林 (RF),KNN和天真贝叶斯 (NB) 分类器对脊柱形状 (正常,C形,S形) 的分类.
主要成果:
- 在0.5的交叉点上,DETR实现了0.96的平均平均精度 (mAP),超过了本地化的欧盟 (IOU) 值.
- 对于脊柱图像分割,SegFormer获得了0.93的子得分.
- 随机森林分类器,使用DETR和SegFormer的特征,在AASCE MICCAI 2019数据集上为脊椎形分类提供了最高的准确性 (98.3%).
- 拟议的方法显示了2.7的低平均绝对误差 (MAE) 和4.37.7的对称平均绝对百分比误差 (SMAPE).
结论:
- 使用DETR和SegFormer的综合方法为自动化脊柱分析提供了强大而准确的方法.
- 随机森林分类器显著提高了脊柱形分类的准确性.
- 这种由人工智能驱动的系统有可能改善脊柱疾病的诊断和管理.
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