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Development of novel techniques for tracking surgical tools

Development of novel techniques for tracking surgical tools
开发追踪手术工具的新技术
批准号:
549831-2020
负责人:
Rivaz, Hassan
金额:
$2.19万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31

项目摘要

项目成果

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中文摘要
翻译
这个项目旨在解决我们目前在手术工具的可靠定位和跟踪方面面临的两个问题,这在矫形外科手术中是至关重要的。为了定位和跟踪手术工具,Think Surgical开发了一种新型的六摄像头系统来定位连接到每个工具上的四个标记(称为基准点)。目标1侧重于减少跟踪误差,这些误差要么是由于基准标记移出跟踪摄像机的视场,要么是由于一个物体(如操作员的手)遮挡了视场内的一个摄像机。我们建议将工具如何移动的物理学与六摄像头系统的感觉数据融合起来,以解决这些问题。物理先验信息包括工具的速度和加速度不可能是无穷大的事实。目的2重点检测由部分覆盖基准点的物体引起的手术工具的不准确定位。例子包括一滴血或擦洗,这大大增加了六摄像头系统的跟踪误差。我们提出了两种方法来解决这个问题。首先,我们将在目标1中开发的技术基础上检测不准确的感官数据。如果所有或少数摄像机定位的基准误差较大,则会提醒用户检查和清理该基准。其次,我们将开发一种机器学习技术,自动将基准的每个相机图像分类为准确或不准确。 这种研究伙伴关系可以导致新技术的开发,使手术工具能够更准确和可靠地定位,潜在地改善整形外科手术的结果。它还将导致培训两名与一家加拿大公司密切合作的高素质人员(HQP)。
英文摘要
This project aims to solve two issues we currently face in reliable localization and tracking of surgical tools, which is of paramount importance in orthopedic surgeries. To localize and track surgical tools, Think Surgical has developed a novel six-camera system to localize four markers (called fiducials) that are attached to each tool. Aim 1 focuses on reducing tracking error caused either by a transition of a fiducial marker out of the field of view of a tracking camera, or by an object (such as operator's hand) occluding one of the cameras within the field of view. We propose to fuse the physics of how the tools can move with sensory data of the six-camera system to address these issues. The physical prior information includes the fact that the velocity and acceleration of the tool cannot be infinity. Aim 2 focuses on detecting inaccurate localization of the surgical tools caused by an object partially covering a fiducial. Examples include a drop of blood or scrubs, which substantially increase the tracking error of the six-camera system. We propose two approaches to tackle this issue. First, we will build on the technique developed in Aim 1 to detect inaccurate sensory data. If all or few cameras localize a fiducial with a large error, the user will be alerted to inspect and clean that fiducial. Second, we will develop a machine learning technique to automatically classify each camera image of a fiducial as accurate or inaccurate. This research partnership can lead to development of novel technologies that enable more accurate and reliable localization of the surgical tools, potentially improving the outcome of orthopedic surgeries. It will also lead to training of two highly qualified personnel (HQP) working in close collaboration with a Canadian company.
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Making Sense of the Data Trove Hidden in Medical Ultrasound Signals
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