Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients

• URL: http://arxiv.org/abs/2102.01059v1
• Date: Mon, 1 Feb 2021 18:49:23 GMT
• Title: Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients
• Authors: Hamideh Kerdegari, Phung Tran Huy Nhat, Angela McBride, VITAL Consortium, Reza Razavi, Nguyen Van Hao, Louise Thwaites, Sophie Yacoub, Alberto Gomez
• Abstract summary: We propose a novel methodology to automatically detect and localize B-lines in lung ultrasound (LUS) videos. We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network and a temporal attention mechanism. 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%.
• Score: 0.6775616141339018
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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