Higher Order of Motion Magnification for Vessel Localisation in Surgical Video

TL;DR

This paper introduces a higher-order motion magnification technique based on physiological artery models to better highlight vascular pulsations in surgical videos, improving vessel visualization during surgery.

Contribution

The paper extends existing motion magnification methods by incorporating higher-order motion (jerk) based on artery physiology, enhancing vessel detection in surgical videos.

Findings

Enhanced visualization of vascular pulse waves.

More accurate motion amplification at higher magnifications.

Improved similarity metrics compared to lower-order methods.

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.