Janatka, M;
Marcus, HJ;
Dorward, NL;
Stoyanov, D;
(2020)
Surgical Video Motion Magnification with Suppression of Instrument Artefacts.
In: Martel, A and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, M and Zhou, SK and Racoceanu, D and Joskowicz, L, (eds.)
MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.
(pp. pp. 353-363).
Springer: Lima, Peru.
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Abstract
Video motion magnification can make blood vessels in surgical video more apparent by exaggerating their pulsatile motion and could prevent inadvertent damage and bleeding due to their increased prominence. It could also indicate the success of restricting blood supply to an organ when using a vessel clamp. However, the direct application to surgical video could result in aberration artefacts caused by its sensitivity to residual motion from the surgical instruments and would impede its practical usage in the operating theatre. By storing the previously obtained jerk filter response of each spatial component of each image frame - both prior to surgical instrument introduction and adhering to a Eulerian frame of reference - it is possible to prevent such aberrations from occurring. The comparison of the current readings to the prior readings of a single cardiac cycle at the corresponding cycle point, are used to determine if motion magnification should be active for each spatial component of the surgical video at that given point in time. In this paper, we demonstrate this technique and incorporate a scaling variable to loosen the effect which accounts for variabilities and misalignments in the temporal domain. We present promising results on endoscopic transnasal transsphenoidal pituitary surgery with a quantitative comparison to recent methods using Structural Similarity (SSIM), as well as qualitative analysis by comparing spatio-temporal cross sections of the videos and individual frames.
| Type: | Proceedings paper |
|---|---|
| Title: | Surgical Video Motion Magnification with Suppression of Instrument Artefacts |
| Event: | MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention |
| ISBN-13: | 9783030597153 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-59716-0_34 |
| Publisher version: | https://doi.org/10.1007/978-3-030-59716-0_34 |
| 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: | Motion Magnification · Surgical Visualisation · Augmented Reality · Computer Assisted Interventions · Image Guided Surgery |
| UCL classification: | UCL 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 Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology 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/10114261 |
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