基于视觉的物体检测和跟踪从外科显微镜视频用于增强耳植入体手术
Shayan Alvansazyazdi1, Razeen Hussain1, Jan Margeta2
1DIBRIS, University of Genoa, Genoa, Italy.
Cochlear implants international
|January 29, 2026
概括
这项研究介绍了一种基于视觉的跟踪系统,使用YOLOv9和BoT-SORT用于耳植入器手术. 该系统准确地跟踪手术工具和患者解剖学,提高了手术精度和安全性.
科学领域:
- 医学技术 医学技术 医学技术
- 计算机视觉 计算机视觉 计算机视觉
- 手术机器人手术机器人手术机器人手术机器人
背景情况:
- 耳植入物 (CIs) 治疗严重的听力损失,但需要精确的电极放置,无需视觉指导.
- 在微妙的耳中进行手术导航,对外科医生来说构成了重大挑战.
研究的目的:
- 开发基于视觉的跟踪策略,在CI手术过程中实时监测患者和外科工具的移动.
- 通过提供视觉反来提高外科准确性和安全性.
主要方法:
- 使用YOLOv9和修改后的BoT-SORT算法开发了一个全面的对象检测和跟踪工作流.
- 在in-vivo和ex-vivo骨数据集上训练并验证了该系统.
- 评估了包括工具,电极,手和患者头部位置在内的各种手术元素的性能.
主要成果:
- 基于YOLOv9的框架表现出卓越的性能,实现了0.788的mAP50检测和0.762的细分.
- 综合系统保持了稳定的身份保护和轨迹一致性.
- 表现在ex vivo和in vivo环境之间有所不同,突出了翻译方面的挑战.
结论:
- 基于视觉的跟踪模型显示了改善外科设置的重大前景,特别是在CI手术中.
- YOLOv9和BoT-SORT系统为现实世界手术应用提供了强大的解决方案.
- 需要进一步的研究来优化不同外科环境的性能.
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