The comparison of OpenCV library algorithms for tracking movements of liver tumors in ultrasound video.

Abstract

This study aims to compare the tracking algorithms provided by the OpenCV library to use on ultrasound video. Despite the widespread application of this computer vision library, few works describe the attempts to use it to track the movement of liver tumors on ultrasound video. Movements of the neoplasms caused by the patient`s breath interfere with the positioning of the instruments during the process of biopsy and radio-frequency ablation. The main hypothesis of the experiment was that tracking neoplasms and correcting the position of the manipulator in case of using robotic-assisted surgery will allow positioning the instruments more precisely. Another goal of the experiment was to check if it is possible to ensure real-time tracking with at least 25 processed frames per second. OpenCV version 4.5.0 was used with 7 tracking algorithms from the extra modules package. They are: Boosting, CSRT, KCF, MedianFlow, MIL, MOSSE, TLD. More than 4900 frames of standard definition were processed during the experiment. Analysis of the results shows that two algorithms – CSRT and KCF – could solve the problem of tumor tracking. They lead the test with more than 70% of ROI coverage and more than 95% successful searches. They could also be used in real-time processing with an average processing speed of up to 30 frames per second in CSRT and up to 100 frames per second for KCF. This experiment also shows that no frames made CSRT and KCF algorithms fail simultaneously. So, the hypothesis for future work is combining these algorithms to work together, with one of them – CSRT – as support for the KCF tracker on the rarely failed frames.