Back to all papers

Lung Ultrasound Imaging Dataset for Accurate Detection and Localization of LUS Vertical Artifact.

Authors

Okila N,Katumba A,Nakatumba-Nabende J,Murindanyi S,Mwikirize C,Serugunda J,Bugeza S,Oriekot A,Bossa J,Nabawanuka E

Affiliations (8)

  • Department of Computer Science, Makerere University, Kampala, Uganda.
  • Department of Electrical and Computer Engineering, Makerere University, Kampala, Uganda. [email protected].
  • Emergent AI, Kampala, Uganda. [email protected].
  • Emergent AI, Kampala, Uganda.
  • Department of Electrical and Computer Engineering, Makerere University, Kampala, Uganda.
  • Department of Radiology, Makerere University Hospital, Makerere University, Kampala, Uganda.
  • Mulago Specialized Women and Neonatal Hospital, Kampala, Uganda.
  • Mulago National Referral Hospital, Kampala, Uganda.

Abstract

Lung ultrasound (LUS) vertical artifacts are critical sonographic markers commonly used in evaluating pulmonary conditions such as pulmonary edema, interstitial lung disease, pneumonia, and COVID-19. Accurate detection and localization of these artifacts are vital for informed clinical decision-making. However, interpreting LUS images remains highly operator-dependent, leading to variability in diagnosis. While deep learning (DL) models offer promising potential to automate LUS interpretation, their development is limited by the scarcity of annotated datasets specifically focused on vertical artifacts. This study introduces a curated dataset of 401 high-resolution LUS images, each annotated with polygonal bounding boxes to indicate vertical artifact locations. The images were collected from 152 patients with pulmonary conditions at Mulago and Kiruddu National Referral Hospitals in Uganda. This dataset serves as a valuable resource for training and evaluating DL models designed to accurately detect and localize LUS vertical artifacts, contributing to the advancement of AI-driven diagnostic tools for early detection and monitoring of respiratory diseases.

Topics

ArtifactsLungLung DiseasesJournal ArticleDataset

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.