Higher Order of Motion Magnification for Vessel Localisation in Surgical Video
This work addresses the critical need for surgeons to avoid inadvertent damage by improving vessel localization in surgical video, representing an incremental advancement in motion magnification techniques.
The paper tackled the problem of locating vessels in surgical video by extending motion magnification to incorporate higher orders of motion based on a physiological model of artery distension, resulting in a method that accentuates cardio-physiological features and produces clearer videos with more similarities to the source at large magnifications, validated using SSIM and PSNR metrics.
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.