Automatic localization of lung opacity in chest CT images: a real-world study

Yiting Xie; Deepta Rajan; Larissa Schudlo; Yusuke Takeuchi; Benedikt Graf; Adam Coy; Mohammadreza Negahdar; Vandana Mukherjee; David Beymer; Arun Krishnan

doi:10.1117/12.2582197

15 February 2021 Automatic localization of lung opacity in chest CT images: a real-world study

Yiting Xie, Deepta Rajan, Larissa Schudlo, Yusuke Takeuchi, Benedikt Graf, Adam Coy, Mohammadreza Negahdar, Vandana Mukherjee, David Beymer, Arun Krishnan

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159707 (2021) https://doi.org/10.1117/12.2582197
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Lung opacities on CT scans, such as ground-glass opacities (GGOs) and consolidation, can manifest with various conditions, including lung cancers, pulmonary edema and COVID-19. Presentation of these non-specific findings can vary from isolated focal to diffuse opacities in all lobes. Moreover, disease distributions and progressions vary across disease types and patients. This unpredictability can challenge one’s ability to accurately quantify and compare the percentage of infected lung within and across patients. Despite the promise of AI models for image segmentation, the inconsistency of lung opacities, and limited access to large annotated datasets affect generalization performance of models to the cohort of lung diseases. In this paper, we developed a single-stage system to jointly localize the lung and opacifications in CT scans using a diverse real-world dataset with sparse annotations. A multi-class Dense U-Net model was designed to segment the lungs and two classes of opacity regions (GGOs and consolidations) in CT images. The model was trained on 4075 slices from 495 sparsely annotated CT studies and evaluated on 18625 slices from 103 densely annotated studies (37 positive). A comparative analysis of different training data subsets and loss functions was performed to determine optimal model design. Performance was evaluated by comparing manual and automated lung opacity percentages via Pearson Correlation Coefficient. The optimal model achieved a Pearson Correlation Coefficient of 0.99. These findings suggest the potential of developing an accurate method to localize lung opacification, unspecific to a particular disease

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Yiting Xie, Deepta Rajan, Larissa Schudlo, Yusuke Takeuchi, Benedikt Graf, Adam Coy, Mohammadreza Negahdar, Vandana Mukherjee, David Beymer, and Arun Krishnan "Automatic localization of lung opacity in chest CT images: a real-world study", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159707 (15 February 2021); https://doi.org/10.1117/12.2582197

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