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三维超声心动图左心室分段算法的标准化评估系统。

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

出版信息

IEEE Trans Med Imaging. 2016 Apr;35(4):967-77. doi: 10.1109/TMI.2015.2503890. Epub 2015 Nov 25.

DOI:10.1109/TMI.2015.2503890
PMID:26625409
Abstract

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

摘要

实时 3 维超声心动图(RT3DE)已被证明是一种评估左心室(LV)容积的准确工具。然而,LV 心内膜的识别仍然是一项具有挑战性的任务,主要是因为图像的组织/血液对比度低,加上典型的伪影。已经提出了几种半自动和全自动算法来分割 RT3DE 数据中的心内膜,以提取相关的临床指数,但由于缺乏公共可用的通用数据库,到目前为止,还不可能对这些方法进行系统和公平的比较。在这里,我们引入了一个标准化的评估框架,以可靠地评估和比较为分割 RT3DE 中 LV 边界而开发的算法的性能。提供了一个由 45 个多供应商心脏超声记录组成的数据库,这些记录是在不同中心采集的,并附有三位专家的相应参考测量值。使用提出的在线平台对来自九个研究小组的算法进行了定量评估和比较。结果表明,对于从专家测量值中提取临床指数,最好的方法产生了有希望的结果,并且在专家可变性范围内,根据平均距离误差,它们提供了良好的分割精度。该平台仍然对新提交内容开放。

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