Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represent a healthy lung surface, B-line artifacts correlate with a wide range of pathological conditions affecting the lung parenchyma. In fact, the appearance of B-lines correlates to an increase in extravascular lung water, interstitial lung diseases, cardiogenic and non-cardiogenic lung edema, interstitial pneumonia and lung contusion. Detection and localization of B-lines in a LUS video are therefore tasks of great clinical interest, with accurate, objective and timely evaluation being critical. This is particularly true in environments such as the emergency units, where timely decision may be crucial. In this work, we present and describe a method aimed at supporting clinicians by automatically detecting and localizing B-lines in an ultrasound scan. To this end, we employ modern deep learning strategies and train a fully convolutional neural network to perform this task on B-mode images of dedicated ultrasound phantoms in-vitro, and on patients in-vivo. An accuracy, sensitivity, specificity, negative and positive predictive value equal to 0.917, 0.915, 0.918, 0.950 and 0.864 were achieved in-vitro, respectively. Using a clinical system in-vivo, these statistics were 0.892, 0.871, 0.930, 0.798 and 0.958, respectively. We moreover calculate neural attention maps that visualize which components in the image triggered the network, thereby offering simultaneous weakly-supervised localization. These promising results confirm the capability of the proposed method to identify and localize the presence of B-lines in clinical lung ultrasonography.

中文翻译：

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通过弱监督深度学习在体内进行肺超声B线定位。

如今，肺超声（LUS）越来越受到临床和技术界的关注。特别令人感兴趣的是几个成像伪像，例如A线和B线伪像。虽然A线是基本上代表健康的肺表面的视觉模式，但B线伪影与影响肺实质的各种病理状况相关。实际上，B线的出现与血管外肺水，间质性肺疾病，心源性和非心源性肺水肿，间质性肺炎和肺挫伤的增加有关。因此，LUS视频中B线的检测和定位是具有重大临床意义的任务，准确，客观和及时的评估至关重要。在紧急情况等至关重要的环境中，例如紧急情况单元尤其如此。在这项工作中，我们提出并描述了一种旨在通过在超声扫描中自动检测和定位B线来支持临床医生的方法。为此，我们采用了现代的深度学习策略，并训练了一个完全卷积的神经网络，以在体外和在患者体内对专用超声体模的B模式图像执行此任务。体外获得的准确度，敏感性，特异性，阴性和阳性预测值分别为0.917、0.915、0.918、0.950和0.864。使用体内临床系统，这些统计分别为0.892、0.871、0.930、0.798和0.958。此外，我们还计算了神经注意图，以可视化图像中的哪些成分触发了网络，从而同时提供了弱监督的定位。