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Image registration and atlas-based segmentation of cardiac outflow velocity profiles

机译:心脏流出速度分布图的图像配准和基于图集的分割

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摘要

Cardiovascular disease is the leading cause of death worldwide and for this reason computer-based diagnosis of cardiac diseases is a very important task. In this article, a method for segmentation of aortic outflow velocity profiles from cardiac Doppler ultrasound images is presented. The proposed method is based on the statistical image atlas derived from ultrasound images of healthy volunteers. The ultrasound image segmentation is done by registration of the input image to the atlas, followed by a propagation of the segmentation result from the atlas onto the input image. In the registration process, the normalized mutual information is used as an image similarity measure, while optimization is performed using a multiresolution gradient ascent method. The registration method is evaluated using an in-silico phantom, real data from 30 volunteers, and an inverse consistency test. The segmentation method is evaluated using 59 images from healthy volunteers and 89 images from patients, and using cardiac parameters extracted from the segmented image. Experimental validation is conducted using a set of healthy volunteers and patients and has shown excellent results. Cardiac parameter segmentation evaluation showed that the variability of the automated segmentation relative to the manual is comparable to the intra-observer variability. The proposed method is useful for computer aided diagnosis and extraction of cardiac parameters.
机译:心血管疾病是全球范围内主要的死亡原因,因此,基于计算机的心脏疾病诊断是一项非常重要的任务。在本文中,提出了一种从心脏多普勒超声图像中分割主动脉流出速度曲线的方法。所提出的方法是基于从健康志愿者的超声图像得出的统计图像图集。通过将输入图像配准到地图集来完成超声图像分割,然后将分割结果从地图集传播到输入图像上。在配准过程中,将规范化的互信息用作图像相似性度量,同时使用多分辨率梯度上升方法执行优化。使用计算机内幻影,来自30名志愿者的真实数据以及逆一致性测试来评估注册方法。使用来自健康志愿者的59张图像和来自患者的89张图像,并使用从分割图像中提取的心脏参数,评估分割方法。使用一组健康的志愿者和患者进行了实验验证,并显示了出色的结果。心脏参数分割评估显示,相对于手册,自动分割的可变性与观察者内部的可变性相当。该方法可用于计算机辅助诊断和心脏参数的提取。

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