Automatic B-line detection in paediatric lung ultrasound

Anantrasirichai, Nantheera; Allinovi, Marco; Hayes, Wesley; Achim, Alin

doi:10.1109/ultsym.2016.7728620

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…In this paper, we propose a novel solution to an inverse problem for line detection in ultrasound images. This extends from our previous work [22] , where lines in noisy ultrasound images were modelled via a Radon transform only and they were estimated using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\ell _{1}$ \end{document} regularisation. Here, we combine a Radon transform with the point spread function (PSF) of the ultrasound acquisition system in a single equation thereby achieving line detection and deconvolution simultaneously.…”

Section: Introductionmentioning

confidence: 83%

“…If the step size for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\theta $ \end{document} is large, e.g. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\Delta \theta > 1^\circ $ \end{document} , a smoothness term \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma || \nabla \mathcal {C} x||_{1}$ \end{document} should be included in (7) to suppress the quantisation noise, which is due to the discrete predefined range of orientations \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\Theta $ \end{document} [22] .…”

Section: Proposed Line Restoration In Speckle Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Line Detection as an Inverse Problem: Application to Lung Ultrasound Imaging

Anantrasirichai

Hayes

Allinovi

et al. 2017

IEEE Trans. Med. Imaging

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This paper presents a novel method for line restoration in speckle images. We address this as a sparse estimation problem using both convex and non-convex optimization techniques based on the Radon transform and sparsity regularization. This breaks into subproblems, which are solved using the alternating direction method of multipliers, thereby achieving line detection and deconvolution simultaneously. We include an additional deblurring step in the Radon domain via a total variation blind deconvolution to enhance line visualization and to improve line recognition. We evaluate our approach on a real clinical application: the identification of B-lines in lung ultrasound images. Thus, an automatic B-line identification method is proposed, using a simple local maxima technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Using all initially detected lines as a starting point, our approach then differentiates between B-lines and other lines of no clinical significance, including Z-lines and A-lines. We evaluated our techniques using as ground truth lines identified visually by clinical experts. The proposed approach achieves the best B-line detection performance as measured by the F score when a non-convex \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\ell _{\text {p}}$ \end{document} regularization is employed for both line detection and deconvolution. The F scores as well as the receiver operating characteristic (ROC) curves show that the proposed approach outperforms the state-of-the-art methods with improvements in B-line detection performance of 54%, 40%, and 33% for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\text {F}}_{0.5}$ \end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\text {F}}_{1}$ \end{document}, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\text {F}}_{2}$ \end{document}, respectively, and of 24% based on ROC curve evaluations.

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Section: Introductionmentioning

confidence: 83%

Section: Proposed Line Restoration In Speckle Imagesmentioning

confidence: 99%

Line Detection as an Inverse Problem: Application to Lung Ultrasound Imaging

Anantrasirichai

Hayes

Allinovi

et al. 2017

IEEE Trans. Med. Imaging

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“…If these five features exceeded predefined thresholds, the image column is a B-line severity associated with it. However, as per Anantrasirichai et al (2016) this method is not robust as it is prone to being greatly affected by noise and image intensity meaning the threshold values must be changed depending on the quality of the images being analyzed. Table 1 provides a comparison of the accuracies of assorted classification methods found in literature.…”

Section: Stochastic Classificationmentioning

confidence: 99%

Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound

McDermott

Łącki

Sainsbury³

et al. 2021

Front. Big Data

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The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.

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“…We employed the B-line detection algorithm proposed in [7] (Section 3.2) to automatically count the number of B-lines in lung ultrasound images. Briefly, this algorithm detects the line artefacts in a Radon transform domain using a local maxima detection method.…”

Section: In Vivo Images -Lung Ultrasoundsmentioning

confidence: 99%

“…We show results of line restoration in both simulated US images and in vivo B-mode US images. The automated B-line detection method [7] was applied to the results in order to investigate the performance in real applications.…”

Section: Introductionmentioning

confidence: 99%