Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications

Abstract

A method for vessel segmentation and tracking in ultrasound images using Kalman filters is presented. A modified Star-Kalman algorithm is used to determine vessel contours and ellipse parameters using an extended Kalman filter with an elliptical model. The parameters can be used to easily calculate the transverse vessel area which is of clinical use. A temporal Kalman filter is used for tracking the vessel center over several frames, using location measurements from a handheld sensorized ultrasound probe. The segmentation and tracking have been implemented in real-time and validated using simulated ultrasound data with known features and real data, for which expert segmentation was performed. Results indicate that mean errors between segmented contours and expert tracings are on the order of 1%-2% of the maximum feature dimension, and that the transverse cross-sectional vessel area as computed from estimated ellipse parameters a, b as determined by our algorithm is within 10% of that determined by experts. The location of the vessel center was tracked accurately for a range of speeds from 1.4 to 11.2 mm/s.

Snakes With an Ellipse-Reproducing Property

“…The computational cost is dominated by the evaluation of the surface integrals in (22) and (23). An efficient way to implement these operations is the use of preintegrated images.…”

“…1 how our snake can adopt the shape of a perfect ellipse (i.e., reproduces the ellipse) as well as more refined shapes. Segmenting circles and ellipses in images is a problem that arises in many fields, such as biomedical engineering [19]- [22] or computer graphics [23], [24]. In medical imaging in particular, it is usually necessary to segment arteries and veins within tomographic slices [25].…”

Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

“…The goal of vessel detection in this work, was to identify the position and size of blood vessels in the image. Several segmentation and tracking methods require this as an initialization [1,6]. In [12], a real-time vessel detection method was introduced, removing the need for manual initialization.…”

Probabilistic Tracking and Model-Based Segmentation of 3D Tubular Structures

“…Often, a simple forward propagation method is utilized where the predicted position of the next increment is directly based on the segmentation result of the current increment, usually proceeding a short distance along the local direction of a vessel (e.g., [4]). A more robust prediction method is the Kalman filter, which assumes a linear measurement model (e.g., [5,6]). A promising alternative method are particle filters, which are more general than the Kalman filter (e.g., inclusion of a nonlinear measurement model) and exploit more effectively the image information (e.g., [7,8,9]).…”

“…A promising alternative method are particle filters, which are more general than the Kalman filter (e.g., inclusion of a nonlinear measurement model) and exploit more effectively the image information (e.g., [7,8,9]). Regarding the measurement model, for tracking schemes based on forward propagation or a Kalman filter, typically contour information (e.g., [6]) or intensity information (e.g., [4,5]) is used. For approaches employing particle filters, different measurement models have been used, e.g., based on circular shortest path search [7], gradient-based shape detection [9], or maximization of the image contrast [8].…”