Conference proceeding
Unsupervised Clustering Of Airway Tree Structures On High-Resolution CT: The Mesa Lung Study
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Vol.2021-, pp.1568-1572
04/13/2021
DOI: 10.1109/ISBI48211.2021.9434172
Abstract
The morphology of the proximal human airway tree is highly variable in the general population, and known variants in airway branching patterns are associated with increased risk of COPD and with polymorphisms in growth factors involved in pulmonary development. Variation in the geometry and topology of the airway tree remains incompletely characterized, and their clinical implications are not yet understood. In this work, we present an approach to unsupervised clustering of airway tree structures in Billera-Holmes-Vogtmann tree-space. We validate our pipeline on synthetic airway tree data, and apply our algorithm to identify reproducible and morphologically distinct airway tree subtypes in the MESA Lung CT cohort.
Details
- Title: Subtitle
- Unsupervised Clustering Of Airway Tree Structures On High-Resolution CT: The Mesa Lung Study
- Creators
- Artur Wysoczanski - Columbia UniversityElsa D Angelini - Columbia UniversityBenjamin M Smith - Columbia University Irving Medical CenterEric A Hoffman - University of IowaGrant T Hiura - Columbia University Medical CenterYifei Sun - Columbia UniversityR. Graham Barr - Columbia University Irving Medical CenterAndrew F Laine - Columbia University
- Resource Type
- Conference proceeding
- Publication Details
- 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Vol.2021-, pp.1568-1572
- DOI
- 10.1109/ISBI48211.2021.9434172
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Grant note
- National Center for Advancing Translational Sciences (10.13039/100006108)
- Language
- English
- Date published
- 04/13/2021
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
- Record Identifier
- 9984318792602771
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