Tracking of the Internal Jugular Vein in Ultrasound Images Using Optical Flow

TL;DR

This paper explores using optical flow, specifically the Lucas-Kanade algorithm, for tracking the internal jugular vein in ultrasound images to improve blood volume estimation despite noise challenges.

Contribution

It demonstrates that the Lucas-Kanade optical flow algorithm outperforms other methods in tracking the internal jugular vein in noisy ultrasound images.

Findings

Lucas-Kanade algorithm provides the best tracking performance.

Optical flow can effectively handle speckle noise and shadowing.

Improved vein tracking aids in blood volume estimation.

Abstract

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from ultrasound imagery of the of the internal jugular vein (IJV). However, segmentation and tracking of the IJV is significantly influenced by speckle noise and shadowing which introduce uncertainty in the boundaries of the vessel. In this paper, we investigate the use of optical flow algorithms for segmentation and tracking of the IJV and show that the classical Lucas-Kanade (LK) algorithm provides the best performance among well-known flow tracking algorithms.