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A-MFST:适应性多流稀疏追踪器,用于在闭塞下实时组织追踪
Yuxin Chen1, Zijian Wu2, Adam Schmidt3
1Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, V6T 1Z4, BC, Canada. yuxinchen@ece.ubc.ca.
概括
这项研究通过改善阻塞处理来增强机器人辅助手术的实时组织跟踪. 新型号在遮蔽期间提高了准确性和可靠性,而不会牺牲速度.
科学领域:
- 医疗机器人 医疗机器人
- 计算机视觉 计算机视觉
- 手术技术 手术技术
背景情况:
- 精确的组织跟踪对于机器人辅助手术至关重要.
- 像SENDD (稀疏有效神经深度和变形) 这样的现有模型在实时跟踪方面表现出色,但在遮方面扎.
- 改善阻塞处理对于手术应用的可靠性至关重要.
研究的目的:
- 扩展SENDD模型,以改善阻塞检测和跟踪一致性.
- 为了保持实时性能,同时解决处理封闭的手术场景的局限性.
- 为了提高在动态的外科环境中稀疏点跟踪的稳定性和准确性.
主要方法:
- 整合分段任何模型 (SAM2) 来检测和掩盖由手术仪器引起的闭塞.
- 开发和整合一个自适应的多流稀疏追踪器 (A-MFST),这是多流密集追踪器 (MFT) 的无监督变体.
- 在A-MFST中实施前向后向一致性指标,以加强封闭和不确定性估计.
主要成果:
- 在遮蔽下,跟踪精度显著提高,平均终点误差 (MEE) 减少了12%.
- 平均精度比指定的像素值提高了6%,表明精度提高.
- 前向后向一致性成功减少了跟踪路径漂移,并改善了整体稳定性,而不影响实时功能.
结论:
- 改进的SENDD模型,包括A-MFST和SAM2,在仪器和组织遮时有效地实时跟踪组织.
- 结合的方法通过强大的阻塞处理和最佳的框架选择来提高跟踪精度和可靠性.
- A-MFST模型减少了跟踪错误,并保持了实时性能,使其适用于苛刻的外科应用.
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