CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and...

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Veröffentlicht in:Scientific reports Jg. 11; H. 1; S. 1455 - 12
Hauptverfasser: Gerard, Sarah E., Herrmann, Jacob, Xin, Yi, Martin, Kevin T., Rezoagli, Emanuele, Ippolito, Davide, Bellani, Giacomo, Cereda, Maurizio, Guo, Junfeng, Hoffman, Eric A., Kaczka, David W., Reinhardt, Joseph M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 14.01.2021
Nature Publishing Group
Nature Portfolio
Schlagworte:
631/114/1305
631/114/1564
Aeration
Algorithms
Automation
Chronic obstructive pulmonary disease
Computed tomography
Coronaviruses
COVID-19
COVID-19 - diagnostic imaging
Female
Humanities and Social Sciences
Humans
Image processing
Inflammation
Lung - diagnostic imaging
Lung cancer
Lungs
Male
multidisciplinary
Neural networks
Neural Networks, Computer
Phenotypes
Pulmonary Fibrosis - diagnostic imaging
SARS-CoV-2
Science
Science (multidisciplinary)
Segmentation
Tomography, X-Ray Computed
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of 0.495 ± 0.309 mm and Dice coefficient of 0.985 ± 0.011 . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-80936-4