Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos

TL;DR

This paper introduces an adaptive polar active contour algorithm that improves segmentation and tracking of the internal jugular vein in ultrasound videos by dynamically adjusting parameters based on previous frames, enhancing accuracy.

Contribution

The novel adaptive polar active contour (Ad-PAC) algorithm dynamically adjusts parameters for better segmentation and tracking in ultrasound videos, outperforming existing methods.

Findings

Significant improvement over state-of-the-art active contour algorithms.

Applied to 65 videos from 13 subjects with 450 frames each.

Enhanced spatial and temporal segmentation accuracy.

Abstract

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a 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 the cross-sectional area (CSA) of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters such as the image quality, shape, and temporal variation. In this paper, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm is applied to 65 ultrasound videos captured from 13 healthy subjects, with each video containing 450 frames. The results show that spatial and temporal adaptation of the energy function significantly improves segmentation performance when compared to current state-of-the-art active contour algorithms.