Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization.
IEEE Trans Ultrason Ferroelectr Freq Control
; 67(11): 2218-2229, 2020 11.
Article
in English
| MEDLINE | ID: covidwho-889664
ABSTRACT
In this article, we present a novel method for line artifacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a nonconvex regularization 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 artifacts. Despite being nonconvex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method, and accurately identifies both horizontal and vertical line artifacts in LUS images. 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Image Interpretation, Computer-Assisted
/
Ultrasonography
/
Coronavirus Infections
/
Lung
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
IEEE Trans Ultrason Ferroelectr Freq Control
Journal subject:
Nuclear Medicine
Year:
2020
Document Type:
Article
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