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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: HMD-EgoPose: head-mounted display-based egocentric marker-less tool and hand pose estimation for augmented surgical guidance.
    Author: Doughty M, Ghugre NR.
    Journal: Int J Comput Assist Radiol Surg; 2022 Dec; 17(12):2253-2262. PubMed ID: 35701681.
    Abstract:
    PURPOSE: The success or failure of modern computer-assisted surgery procedures hinges on the precise six-degree-of-freedom (6DoF) position and orientation (pose) estimation of tracked instruments and tissue. In this paper, we present HMD-EgoPose, a single-shot learning-based approach to hand and object pose estimation and demonstrate state-of-the-art performance on a benchmark dataset for monocular red-green-blue (RGB) 6DoF marker-less hand and surgical instrument pose tracking. Further, we reveal the capacity of our HMD-EgoPose framework for performant 6DoF pose estimation on a commercially available optical see-through head-mounted display (OST-HMD) through a low-latency streaming approach. METHODS: Our framework utilized an efficient convolutional neural network (CNN) backbone for multi-scale feature extraction and a set of subnetworks to jointly learn the 6DoF pose representation of the rigid surgical drill instrument and the grasping orientation of the hand of a user. To make our approach accessible to a commercially available OST-HMD, the Microsoft HoloLens 2, we created a pipeline for low-latency video and data communication with a high-performance computing workstation capable of optimized network inference. RESULTS: HMD-EgoPose outperformed current state-of-the-art approaches on a benchmark dataset for surgical tool pose estimation, achieving an average tool 3D vertex error of 11.0 mm on real data and furthering the progress towards a clinically viable marker-free tracking strategy. Through our low-latency streaming approach, we achieved a round trip latency of 199.1 ms for pose estimation and augmented visualization of the tracked model when integrated with the OST-HMD. CONCLUSION: Our single-shot learned approach, which optimized 6DoF pose based on the joint interaction between the hand of a user and a rigid surgical drill, was robust to occlusion and complex surfaces and improved on current state-of-the-art approaches to marker-less tool and hand pose estimation. Further, we presented the feasibility of our approach for 6DoF object tracking on a commercially available OST-HMD.
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