Automatic detection of B-lines in lung ultrasound videos from severe dengue patients

Hamideh Kerdegari, Phung Tran Huy Nhat, Angela McBride, Reza Razavi, Nguyen Van Hao, Louise Thwaites, Sophie Yacoub, Alberto Gomez

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

4 Citations (Scopus)

Abstract

Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including the presence of B-line artefacts due to fluid leakage into the lungs caused by a variety of diseases. However, manual detection of these artefacts is challenging. In this paper, we propose a novel methodology to automatically detect and localize B-lines in LUS videos using deep neural networks trained with weak labels. To this end, we combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network and a temporal attention mechanism. Four different models are compared using data from 60 patients. Results show that our best model can determine whether one-second clips contain B-lines or not with an F1 score of 0.81, and extracts a representative frame with B-lines with an accuracy of 87.5%.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages989-993
Number of pages5
ISBN (Electronic)9781665412469
DOIs
Publication statusPublished - 13 Apr 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period13/04/202116/04/2021

Keywords

  • Classification
  • Lung ultrasound (LUS)
  • Video analysis

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