Undergraduate Research Showcase

Automated Artery and Vein Classification of Pulmonary Vessels
Anthony Luo, anthony.luo@columbia.edu
SEAS ‘23, Computer Science, Columbia University
Supervising Faculty, Sponsor, and Location of Research
Professor Andrew F. Laine, Summer@SEAS
Heffner Biomedical Imaging Lab, Columbia University
Abstract
An accurate and automatic system for artery and vein classification of pulmonary vessels
has the potential to drastically increase efficiency of medical diagnoses and analyses that
require artery and vein labeling of pulmonary vessels. In particular, labeled arteries and
vessels facilitate site isolation for vascular pathology, and assist inferences of causes of
pulmonary vascular dysfunction. We present a software pipeline to perform automatic
artery and vein classification up to a user specified diameter without the need for contrast
enhanced CT or manual seed point initialization. The performance of this software
pipeline is significantly faster than manual labeling by an expert analyst and provides
additional flexibility through vessel diameter and branch length filtering options. Our
pipeline uses an expert system based approach to classification by leveraging the
closeness and co-orientation of pulmonary arteries and bronchi. Compared to prior art,
our system introduces a novel combination of straight-line and per-voxel co-orientation
and co-distance calculations. We plan to compare our pipeline against a semi-automated
region growing approach and manually annotated ground truth.
Keywords
pulmonary vessels, artery vein classification, airways
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