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Semi-Automatic A-Line Detection and Confidence Scoring in Lung Ultrasound

Oriane Thiery
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  • PersonId : 1176247
Garance Martin
Isabelle Bloch
Umar Saleem
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  • PersonId : 1176249
Andrea Pinna
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  • PersonId : 955698


Weaning a patient from mechanical ventilation is a critical task in Intensive Care Units, but it can be made safer by using Lung Ultrasound scoring. This scoring is currently done visually by specialists based on Lung Ultrasound artifacts among which are A-lines. Automating this scoring may help standardizing results and saving time for clinicians. In this paper, we propose a method to automatically detect A-lines from a manual delineation of the pleural line, and by using both the intensity profile of the LUS image and morphological operations. A score is then assigned to significant lines and represents the possibility of them being A-lines. The proposed method shows promising results in differentiating A-lines from other elements with an Area Under the Curve of 0.95; furthermore, using a threshold at 0.5 to detect A-lines leads to very good performances with an accuracy of 95% and a F0.5-score of 0.84.
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hal-03820243 , version 1 (20-10-2022)


  • HAL Id : hal-03820243 , version 1


Oriane Thiery, Garance Martin, Isabelle Bloch, Martin Dres, Umar Saleem, et al.. Semi-Automatic A-Line Detection and Confidence Scoring in Lung Ultrasound. BioCAS 2022 - Biomedical Circuits and Systems Conference, IEEE, Oct 2022, Taipei, Taiwan. ⟨hal-03820243⟩
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