An automated robust segmentation method for intravascular ultrasound images

Abstract

It is widely known that the state of a patient's coronary heart disease can be better assessed using intravascular ultrasound (IVUS) than with more conventional angiography. Recent work has shown that segmentation and 3D reconstruction of IVUS pullback sequence images can be used for computational fluid dynamic simulation of blood flow through the coronary arteries. This map of shear stress in the blood vessel walls can be used to predict susceptibility of a region of the arteries to future arteriosclerosis and disease. Manual segmentation of images is time consuming as well as cost prohibitive for routine diagnostic use. Current segmentation algorithms do not achieve a high enough accuracy because of the presence of speckle due to blood flow, relatively low resolution of images and presence of various artifacts including guide-wires, stents, vessel branches, and some other growth or inflammations. On the other hand, the image may be induced with additional blur due to movement distortions, as well as resolution-related mixing of closely resembling pixels thus forming a type of out-offocus blur. Robust automated segmentation achieving high accuracy of 95% or above has been elusive despite work by a large community of researchers in the machine vision field. In this chapter, we present a comprehensive method, based on computer vision and pattern recognition, where a multitude of algorithms are applied simultaneously to the segmentation problem. The method presented is to combine algorithms using a meta-algorithmic approach. Each segmentation algorithm computes along with the segmentation a measure of confidence in the segmentation which can be biased on prior information about the presence of artifacts. A meta-algorithm then runs a library of algorithms on a sub-sequence of images to be segmented and chooses the segmentation based on computed confidence measures. Machine learning and testing is performed on a large database, that includes 2293 gated image frames that have been manually segmented for training and performance comparison, and a total of 57,098 image frames for testing the meta-algorithm to obtain reliable segmentation performance assessment.