自动化关节空间检测提高了骨段的精度
H Mark Kenney1,2,3, Daniel Lichau4, Rémi Blanc4
1Center for Musculoskeletal Research, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA, 14642.
bioRxiv : the preprint server for biology
|July 14, 2025
概括
深度学习模型从微型CT扫描中显著改善了从微型CT扫描中对老鼠爪解剖的自动细分,优于以前的方法. 这种先进的技术增强了在健康和关节炎条件下复杂的骨结构的研究.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 计算解剖学的计算解剖学
- 深度学习应用程序
背景情况:
- 复杂的解剖结构的手动细分是劳动密集型的,容易变化.
- 数字图像分析,特别是深度学习 (DL),为地标检测提供了自动化解决方案.
- 现有的半自动化 (SA) 方法在准确性和效率方面存在局限性.
研究的目的:
- 开发和评估一个监督的深度学习模型,用于对小鼠脚关节的自动细分.
- 为了比较DL模型与半自动化方法的性能.
- 在健康和关节炎小鼠模型中评估模型的准确性.
主要方法:
- 微型计算机断层扫描 (micro-CT) 数据集的监督分析,对小鼠的后腿和前腿.
- 开发了一个3D U-Net DL模型,配备ResNet-18骨干,用于联合空间预测.
- 整合结构增强,张量投票和输出扩展到细分管道中.
- 使用野生类型 (WT) 后翼标签作为训练和测试的基本真相.
主要成果:
- 与SA方法相比,DL模型在WT小鼠中实现了显著更高的细分精度 (例如,WT雄性:DL 98.16%与SA 79.39%).
- 在患有炎性侵蚀性关节炎的转基因瘤亡因子 (TNF-Tg) 鼠群中,精度下降,与疾病严重程度和年龄相关.
- 前肢细分精度也随着TNF-Tg小鼠关节炎严重程度的增加而下降.
结论:
- 监督的DL模型大大优于SA的方法来细分健康的小鼠脚关节.
- 该模型显示出研究复杂骨解剖学的潜力,但在疾病修饰数据集中表现较差.
- 需要进一步开发以优化DL模型的复杂和病理解剖变异.
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