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

• URL: http://arxiv.org/abs/2108.03138v1
• Date: Fri, 6 Aug 2021 14:17:51 GMT
• Title: Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia
• Authors: Harry Mason, Lorenzo Cristoni, Andrew Walden, Roberto Lazzari, Thomas Pulimood, Louis Grandjean, Claudia AM Gandini Wheeler-Kingshott, Yipeng Hu, Zachary MC Baum
• Abstract summary: We focus on the hyperechoic B-line segmentation task using deep neural networks. We utilize both COVID-19 and CAP lung ultrasound data to train the networks. Segmenting either type of lung condition at inference may support a range of clinical applications.
• Score: 0.17159130619349347
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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