Development of novel machine learning algorithms for registration of point clouds and tracking surgical tools

这种合作关系有两个重点，以解决全膝关节置换术（TKA）中配准和跟踪的两个首要问题。配准和跟踪将确定切割股骨的位置和角度，这是一个无法撤消的操作。因此，这两个方面都至关重要。Thrust 1将专注于稳健的点云配准、分类配准的准确性以及个性化的点选择策略。这一推动力也将在两个目标中实现。在第一个目标中，我们首先开发新的注册技术，在数学上具有挑战性的注册问题。然后，我们将开发新的方法，可以自动评估配准的准确性，使程序只能在配准准确的情况下进行。在第二个目标，我们将开发一种新的采样策略，是个性化的每一个骨骼，使最佳的配准结果可以实现与选择的几个点。在这两个目标中，我们将开发使用传统机器学习（ML）和深度学习（DL）算法的新技术。推力2将专注于手术工具的鲁棒跟踪，并自动检测跟踪是否不准确。这一推动将在两个具体目标中进行，其中第一个目标侧重于经典图像处理和ML技术，第二个目标侧重于DL。除了提高跟踪精度，我们提出的方法将自动检测是否有一个工具没有被准确跟踪，以便外科医生可以清理受影响的工具。这种研究伙伴关系可以导致新技术的发展，使TKA中更准确和可靠的定位和配准，潜在地改善手术的结果。它还将导致培训六名高素质人员，与一家加拿大公司密切合作。

This partnership has two thrusts to solve two overarching problems of registration and tracking in total knee arthroplasty (TKA). Registration and tracking will determine the location and angle for cutting the femur bone, an action that cannot be undone. As such, both thrusts are of critical importance. Thrust 1 will focus on robust point cloud registration, classification of the accuracy of the registration, and personalized point selection strategy. This thrust will also be carried out in two aims. In the first aim, we first develop novel registration techniques for the mathematically challenging problem of registration. We will then develop novel methods that can automatically assess the accuracy of the registration, so that the procedure can only proceed if registration is accurate. In the second aim, we will develop a novel sampling strategy that is personalized to each bone, so that optimal registration results can be achieved with the selection of few points. In both aims of this thrust, we will develop novel techniques that use both traditional Machine Learning (ML) and Deep Learning (DL) algorithms. Thrust 2 will focus on robust tracking of surgical tools, and automatically detecting if tracking is inaccurate. This thrust will be carried out in two specific aims, wherein the first aim focuses on classical image processing and ML techniques, and the second aim focuses on DL. In addition to improving the tracking accuracy, our proposed method will automatically detect if one of the tools is not being accurately tracked, so that the surgeon can clean the affected tool.This research partnership can lead to development of novel technologies that enable more accurate and reliable localization and registration in TKA, potentially improving the outcome of the surgery. It will also lead to training of six highly qualified personnel (HQP) working in close collaboration with a Canadian company.