Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization

O. Karakuş

VI Lab.

University of Bristol

N. Anantrasirichai

VI Lab.

University of Bristol

A. Aguersif

Service de Réanimation

CHU Purpan, Toulouse

S. Silva

Service de Réanimation

CHU Purpan, Toulouse

A. Basarab


University Paul Sabatier

A. Achim

VI Lab.

University of Bristol


This project is concerned with the quantification of line artefacts in lung ultrasound (LUS) images of COVID-19 patients via a non-covex regularisation based methodology.


Engineering and Physical Sciences Research Council (EPSRC) under grant EP/R009260/1

EPSRC Impact Acceleration Award (IAA) from the University of Bristol

Leverhulme Trust Research Fellowship (INFHER)


Service de Réanimation, CHU Purpan, Toulouse

IRIT, University Paul Sabatier Toulouse




In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method, and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method, and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients.



  author={O. {Karakuş} and N. {Anantrasirichai} and A. {Aguersif} and S. {Silva} and A. {Basarab} and A. {Achim}},
  journal={IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control}, 
  title={Detection of Line Artifacts in Lung Ultrasound Images of {COVID-19} Patients Via Nonconvex Regularization},