ArticleProceedings. IEEE International Symposium on Biomedical Imaging2023
MULTI-VIEW CNN FOR TOTAL LUNG VOLUME INFERENCE ON CARDIAC COMPUTED TOMOGRAPHY.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Association of dysanapsis with mortality among older adults.The European respiratory journal · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
Total lung volume (TLV) at full inspiration is a parameter of significant interest in pulmonary physiology but requires computed tomography (CT) scanning of the full axial extent of the lung. There is a growing interest to infer TLV from cardiac CT scans, which are much more widely available in epidemiologic studies. In this study, we present an original approach to train a multi-view convolutional neural network (CNN) model to infer TLV from cardiac CT scans, which visualize about 2/3rd of the lung volume. Supervised learning is used, exploiting paired full-lung and cardiac CT scans in the Multi-Ethnic Study of Atherosclerosis (MESA). Our results show that our network outperforms existing regression models for TLV estimation, and achieves accuracy and reproducibility comparable to the scan-rescan reproducibility of TLV on full-lung CT.
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Registered trials
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