ArticleProceedings. IEEE International Symposium on Biomedical Imaging2019
A SR-NET 3D-TO-2D ARCHITECTURE FOR PARASEPTAL EMPHYSEMA SEGMENTATION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.Scientific reports · 2022Article
- Artificial Intelligence in COPD: New Venues to Study a Complex Disease.Barcelona respiratory network reviewsArticle
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Paraseptal emphysema (PSE) is a relatively unexplored emphysema subtype that is usually asymptomatic, but recently associated with interstitial lung abnormalities which are related with clinical outcomes, including mortality. Previous local-based methods for emphysema subtype quantification do not properly characterize PSE. This is in part for their inability to properly capture the global aspect of the disease, as some the PSE lesions can involved large regions along the chest wall. It is our assumption, that path-based approaches are not well-suited to identify this subtype and segmentation is a better paradigm. In this work we propose and introduce the Slice-Recovery network (
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