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

Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia

Mason, H; Cristoni, L; Walden, A; Lazzari, R; Pulimood, T; Grandjean, L; Wheeler-Kingshott, CAMG; ... Baum, ZMC; + view all (2021) Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia. In: International Workshop on Advances in Simplifying Medical Ultrasound ASMUS 2021: Simplifying Medical Ultrasound. (pp. pp. 45-53). Springer: Cham, Switzerland. Green open access

[thumbnail of 2108.03138v1.pdf]
Preview
Text
2108.03138v1.pdf - Accepted Version

Download (365kB) | Preview

Abstract

Lung ultrasound imaging has been shown effective in detecting typical patterns for interstitial pneumonia, as a point-of-care tool for both patients with COVID-19 and other community-acquired pneumonia (CAP). In this work, we focus on the hyperechoic B-line segmentation task. Using deep neural networks, we automatically outline the regions that are indicative of pathology-sensitive artifacts and their associated sonographic patterns. With a real-world data-scarce scenario, we investigate approaches to utilize both COVID-19 and CAP lung ultrasound data to train the networks; comparing fine-tuning and unsupervised domain adaptation. Segmenting either type of lung condition at inference may support a range of clinical applications during evolving epidemic stages, but also demonstrates value in resource-constrained clinical scenarios. Adapting real clinical data acquired from COVID-19 patients to those from CAP patients significantly improved Dice scores from 0.60 to 0.87 (p < 0.001) and from 0.43 to 0.71 (p < 0.001), on independent COVID-19 and CAP test cases, respectively. It is of practical value that the improvement was demonstrated with only a small amount of data in both training and adaptation data sets, a common constraint for deploying machine learning models in clinical practice. Interestingly, we also report that the inverse adaptation, from labelled CAP data to unlabeled COVID-19 data, did not demonstrate an improvement when tested on either condition. Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.

Type: Proceedings paper
Title: Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia
Event: The 2nd International Workshop of Advances in Simplifying Medical UltraSound (ASMUS)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.1007/978-3-030-87583-1_5
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Deep-learning, Segmentation, Domain adaptation, Lung ultrasound, COVID-19, Pneumonia
UCL classification: UCL
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/10134580
Downloads since deposit
14Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item