Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

By Subhankar Roy, Willi Menapace, S. Oei, Ben Luijten, Enrico Fini, Cristiano Saltori, Iris A. M. Huijben, Nishith Chennakeshava, Federico Mento, Alessandro Sentelli and 12 more. IEEE Transactions on Medical Imaging, 2020

462 citations recorded by the source corpus.

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.

Subfields: Critical Care and Intensive Care Medicine, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1343579da82015726f771402fe0564ec4a165dac

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In Pulmonary and Respiratory Medicine

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Citations in the corpus

Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “Then, given the speciﬁed states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “Ultrasound imaging is also difficult because sound waves reflect strongly at boundaries of air and tissue, but point-of-care ultrasound is used at the emergency department and is widely used to monitor COVID-19 patients for which the first decision support applications based on DL have already appea”