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

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Simplifying Medical Ultrasound (ASMUS 2021)

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.

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Acknowledgments

This work is supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (203145Z/16/Z). C.A.M. Gandini Wheeler-Kingshott is supported by the MS Society (#77), Wings for Life (#169111), Horizon2020 (CDS-QUAMRI, #634541), BRC (#BRC704/CAP/CGW), and allocation from the UCL QR Global Challenges Research Fund (GCRF). Z.M.C. Baum is supported by the Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarships-Doctoral Program, and the University College London Overseas and Graduate Research Scholarships.

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Mason, H. et al. (2021). Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia. In: Noble, J.A., Aylward, S., Grimwood, A., Min, Z., Lee, SL., Hu, Y. (eds) Simplifying Medical Ultrasound. ASMUS 2021. Lecture Notes in Computer Science(), vol 12967. Springer, Cham. https://doi.org/10.1007/978-3-030-87583-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-87583-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87582-4

  • Online ISBN: 978-3-030-87583-1

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