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Higher Order of Motion Magnification for Vessel Identification in Surgical Video

Janatka, MP; Sridhar, A; Kelly, J; Stoyanov, D; (2018) Higher Order of Motion Magnification for Vessel Identification in Surgical Video. In: Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention:MICCAI 2018. (pp. pp. 307-314). Springer, Cham: Granada, Spain. Green open access

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Abstract

Locating vessels during surgery is critical for avoiding inadvertent damage, yet vasculature can be difficult to identify. Video motion magnification can potentially highlight vessels by exaggerating subtle motion embedded within the video to become perceivable to the surgeon. In this paper, we explore a physiological model of artery distension to extend motion magnification to incorporate higher orders of motion, leveraging the difference in acceleration over time (jerk) in pulsatile motion to highlight the vascular pulse wave. Our method is compared to first and second order motion based Eulerian video magnification algorithms. Using data from a surgical video retrieved during a robotic prostatectomy, we show that our method can accentuate cardio-physiological features and produce a more succinct and clearer video for motion magnification, with more similarities in areas without motion to the source video at large magnifications. We validate the approach with a Structure Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) assessment of three videos at an increasing working distance, using three different levels of optical magnification. Spatio-temporal cross sections are presented to show the effectiveness of our proposal and video samples are provided to demonstrates qualitatively our results.

Type: Proceedings paper
Title: Higher Order of Motion Magnification for Vessel Identification in Surgical Video
Event: 21st International Conference on Medical Image Computing and Computer-Assisted Intervention:MICCAI 201
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00937-3_36
Publisher version: https://doi.org/10.1007/978-3-030-00937-3_36
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Video motion magnification, Vessel localisation, Augmented reality, Computer assisted interventions
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10057308
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