Preprint
Unsupervised Airway Tree Clustering with Deep Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
arXiv.org
Cornell University Library, arXiv.org
02/28/2024
DOI: 10.48550/arxiv.2402.18615
Abstract
High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation. The airway binary masks display very complex tree structures that may encode biological information relevant to disease risk and yet remain challenging to exploit via traditional methods such as meshing or skeletonization. Recent clinical studies suggest that some variations in shape patterns and caliber of the human airway tree are highly associated with adverse health outcomes, including all-cause mortality and incident COPD. However, quantitative characterization of variations observed on CT segmented airway tree remain incomplete, as does our understanding of the clinical and developmental implications of such. In this work, we present an unsupervised deep-learning pipeline for feature extraction and clustering of human airway trees, learned directly from projections of 3D airway segmentations. We identify four reproducible and clinically distinct airway sub-types in the MESA Lung CT cohort.
Details
- Title: Subtitle
- Unsupervised Airway Tree Clustering with Deep Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
- Creators
- Sneha Naik - Columbia UniversityElsa Angelini - Columbia UniversityR Graham Barr - Columbia UniversityNorrina Allen - Northwestern UniversityAlain Bertoni - Wake Forest UniversityEric A Hoffman - University of IowaAni Manichaikul - University of VirginiaJim Pankow - University of MinnesotaWendy Post - Johns Hopkins UniversityYifei Sun - Columbia UniversityKarol Watson - University of California, Los AngelesBenjamin M Smith - McGill UniversityAndrew Laine - Columbia University
- Resource Type
- Preprint
- Publication Details
- arXiv.org
- DOI
- 10.48550/arxiv.2402.18615
- eISSN
- 2331-8422
- Publisher
- Cornell University Library, arXiv.org; Ithaca
- Language
- English
- Date posted
- 02/28/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
- Record Identifier
- 9984562759302771
Metrics
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