Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements

• URL: http://arxiv.org/abs/2201.10848v1
• Date: Wed, 26 Jan 2022 10:15:46 GMT
• Title: Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements
• Authors: Lukas Burger, Lalith Sharan, Samantha Fischer, Julian Brand, Maximillian Hehl, Gabriele Romano, Matthias Karck, Raffaele De Simone, Ivo Wolf, Sandy Engelhardt
• Abstract summary: To support minimally-invasive mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus. Hand-eye calibration is required that links both coordinate systems and is a prerequisite to project the points onto the image plane. A complementary approach to this is to use a vision-based endoscopic stereo-setup to detect and triangulate points of interest, to obtain the 3D coordinates. Preliminary results indicate that 3D landmark estimation, either labeled manually or through partly automated detection with a deep learning approach, provides more accurate triangulated depth measurements when performed with a tailored image-based method than
• Score: 1.1084983279967584
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To support minimally-invasive intraoperative mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus. It is desirable to view such manually measured points together with the endoscopic image for further assistance. Therefore, hand-eye calibration is required that links both coordinate systems and is a prerequisite to project the points onto the image plane. A complementary approach to this is to use a vision-based endoscopic stereo-setup to detect and triangulate points of interest, to obtain the 3D coordinates. In this paper, we aim to compare both approaches on a rigid phantom and two patient-individual silicone replica which resemble the intraoperative scenario. The preliminary results indicate that 3D landmark estimation, either labeled manually or through partly automated detection with a deep learning approach, provides more accurate triangulated depth measurements when performed with a tailored image-based method than with stylus measurements.

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