人工智能
分割
深度学习
计算机科学
卷积神经网络
射血分数
编码器
模式识别(心理学)
平均绝对误差
均方误差
医学
心脏病学
数学
统计
心力衰竭
操作系统
作者
Sarah Leclerc,Erik Smistad,João Pedrosa,Andreas Østvik,Frédéric Cervenansky,Florian Espinosa,Torvald Espeland,Erik Andreas Rye Berg,Pierre-Marc Jodoin,Thomas Grenier,Carole Lartizien,Jan D'hooge,Lasse Løvstakken,Olivier Bernard
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2019-09-01
卷期号:38 (9): 2198-2210
被引量:290
标识
DOI:10.1109/tmi.2019.2900516
摘要
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.
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