Image quality assessment for closed-loop computer-assisted lung ultrasound
Image quality assessment for closed-loop computer-assisted lung ultrasound
复制标题
闭环计算机辅助肺部超声图像质量评估
DOI：
10.1117/12.2581865
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Baum Z
中科院分区：
文献类型：
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作者：
Baum Z
我们描述了一种新型的两阶段计算机辅助系统，用于在重症监护环境中使用超声成像进行肺部异常检测，以改善冠状病毒大流行期间的操作员性能和患者分层。该系统由两个基于深度学习的模型组成：一个质量评估模块，用于自动预测图像质量;一个诊断辅助模块，用于确定足够质量的超声图像中的异常可能性。我们的两阶段策略使用新奇检测算法来解决缺乏可用于训练质量评估分类器的控制案例。然后，可以利用被认为具有足够质量的数据来训练诊断辅助模块，所述足够质量由来自质量评估模块的闭环反馈机制保证。使用来自两家医院扫描的37名COVID-19阳性患者的25，000多张超声图像，加上12个对照病例，这项研究证明了使用所提出的机器学习方法的可行性。我们报告的准确性为86%，当足够和不足的质量图像的质量评估模块进行分类。对于质量评估模块确定的足够质量的数据，在拟议系统中的任何网络训练期间未见过的五个保持测试数据集中，检测COVID-19阳性病例的平均分类准确性，灵敏度和特异性分别为0.95，0.91和0.97。总的来说，这两个模块的集成产生了准确，快速和实用的采集指导和诊断援助的病人与可疑的呼吸条件在床旁护理。
We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality – as determined by the quality assessment module – the mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at pointof- care.
影响因子：
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作者：
Pietersen PI;Madsen KR;Graumann O;Konge L;Nielsen BU;Laursen CB
通讯作者：
Laursen CB
影响因子：
38.9
作者：
Volpicelli, Giovanni;Elbarbary, Mahmoud;Petrovic, Tomislav
通讯作者：
Petrovic, Tomislav