Abstract
Lung ultrasound imaging is nowadays receiving growing attention. In fact, the analysis of specific artefactual patterns reveals important diagnostic information. A- and B-line artifacts are particularly important. A-lines are generally considered a sign of a healthy lung, while B-line artifacts correlate with a large variety of pathological conditions. B-lines have been found to indicate an increase in extravascular lung water, the presence of interstitial lung diseases, non-cardiogenic lung edema, interstitial pneumonia and lung contusion.
The capability to accurately and objectively detect and localize B-lines in a lung ultrasound video is therefore of great clinical interest. In this paper, we present a method aimed at supporting clinicians in the analysis of ultrasound videos by automatically detecting and localizing B-lines, in real-time. To this end, modern deep learning strategies have been used and a fully convolutional neural network has been trained to detect B-lines in B-mode images of dedicated ultrasound phantoms. Furthermore, neural attention maps have been calculated to visualize which components in the image triggered the network, thereby offering simultaneous weakly-supervised localization. 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 using data from dedicated lung-mimicking phantoms, respectively.
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Acknowledgement
The authors want to acknowledge M.D. Gino Soldati for the scoring of the ultrasound videos.
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van Sloun, R.J.G., Demi, L. (2019). B-line Detection and Localization by Means of Deep Learning: Preliminary In-vitro Results. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_38
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DOI: https://doi.org/10.1007/978-3-030-27202-9_38
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