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Unsupervised Airway Tree Clustering with Deep Learning: the Multi-Ethnic Study of Atherosclerosis (Mesa) Lung Study
Conference proceeding   Open access

Unsupervised Airway Tree Clustering with Deep Learning: the Multi-Ethnic Study of Atherosclerosis (Mesa) Lung Study

Sneha N. Naik, Elsa D. Angelini, R. Graham Barr, Norrina Allen, Alain Bertoni, Eric A. Hoffman, Ani Manichaikul, Jim Pankow, Wendy Post, Yifei Sun, …
2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.1-5
05/27/2024
DOI: 10.1109/ISBI56570.2024.10635651
PMCID: PMC11467912
PMID: 39398280
url
https://arxiv.org/pdf/2402.18615View
Open Access

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.
 Computed Tomography Airway structure Community Detection Deep learning Image segmentation Lung Lung CT Pipelines Shape Three-dimensional displays

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