Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound
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DOI：
10.1109/tmi.2020.2994459
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发表时间：
2020-08-01
影响因子：
10.6
通讯作者：
Demi, Libertario
中科院分区：
文献类型：
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作者：
Roy, Subhankar;Menapace, Willi;Demi, Libertario
深度学习（DL）已被证明在医学成像中是成功的，在最近的COVID-19大流行之后，一些工作已经开始研究基于DL的肺部疾病辅助诊断解决方案。虽然现有的工作集中在CT扫描上，但本文研究了DL技术在肺部超声（LUS）图像分析中的应用。具体来说，我们提出了一种新的完全注释的LUS图像数据集，从几家意大利医院收集，标签表明疾病的严重程度在帧级，视频级和像素级（分割掩模）。利用这些数据，我们引入了几个深度模型，这些模型可以解决LUS图像自动分析的相关任务。特别是，我们提出了一种新的深度网络，来自空间Transformer网络，它同时预测与输入帧相关的疾病严重程度评分，并以弱监督的方式提供病理伪影的定位。此外，我们介绍了一种新的方法，基于uninorms有效的帧分数聚合在视频级。最后，我们对最先进的深度模型进行基准测试，以估计COVID-19成像生物标志物的像素级分割。在所提出的数据集上的实验表明，所有考虑的任务都取得了令人满意的结果，为未来从LUS数据辅助诊断COVID-19的DL研究铺平了道路。
Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.