UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Image quality assessment for closed-loop computer-assisted lung ultrasound

Baum, ZMC; Bonmati, E; Cristoni, L; Walden, A; Prados, F; Kanber, B; Barratt, DC; ... Hu, Y; + view all (2020) Image quality assessment for closed-loop computer-assisted lung ultrasound. ArXiv , Article arXiv:2008.08840v1. (In press).

[img] Text
Baum_SPIE2021_COVID-LUS_Submitted-1.pdf - Accepted version
Access restricted to UCL open access staff until 17 August 2021.

Download (261kB)

Abstract

We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models. A quality assessment module automates predictions of image quality, and a diagnosis assistance module determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training a quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Integrating the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at the point-of-care. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality, the mean classification accuracy in detecting COVID-19-positive cases was 95% on five holdout test data sets, unseen during the training of any networks within the proposed system.

Type: Article
Title: Image quality assessment for closed-loop computer-assisted lung ultrasound
Publisher version: https://arxiv.org/abs/2008.08840v1
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10108957
Downloads since deposit
1Download
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item