B-line detection and localization by means of deep learning: preliminary in-vitro results

Ruud J.G. van Sloun, Libertario Demi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
EditorsFakhri Karray, Alfred Yu, Aurélio Campilho
Place of PublicationCham
Number of pages7
ISBN (Electronic)978-3-030-27202-9
ISBN (Print)978-3-030-27201-2
Publication statusPublished - 8 Aug 2019
Event16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada
Duration: 27 Aug 201929 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11662 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Image Analysis and Recognition, ICIAR 2019


  • B-lines
  • Deep learning
  • Image analysis
  • Lung ultrasound


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