Undergraduate Research Showcase

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Automated Artery and Vein Classification of Pulmonary Vessels<br />

Anthony Luo, anthony.luo@columbia.edu<br />

SEAS ‘23, Computer Science, Columbia University<br />

Supervising Faculty, Sponsor, and Location of <strong>Research</strong><br />

Professor Andrew F. Laine, Summer@SEAS<br />

Heffner Biomedical Imaging Lab, Columbia University<br />

Abstract<br />

An accurate and automatic system for artery and vein classification of pulmonary vessels<br />

has the potential to drastically increase efficiency of medical diagnoses and analyses that<br />

require artery and vein labeling of pulmonary vessels. In particular, labeled arteries and<br />

vessels facilitate site isolation for vascular pathology, and assist inferences of causes of<br />

pulmonary vascular dysfunction. We present a software pipeline to perform automatic<br />

artery and vein classification up to a user specified diameter without the need for contrast<br />

enhanced CT or manual seed point initialization. The performance of this software<br />

pipeline is significantly faster than manual labeling by an expert analyst and provides<br />

additional flexibility through vessel diameter and branch length filtering options. Our<br />

pipeline uses an expert system based approach to classification by leveraging the<br />

closeness and co-orientation of pulmonary arteries and bronchi. Compared to prior art,<br />

our system introduces a novel combination of straight-line and per-voxel co-orientation<br />

and co-distance calculations. We plan to compare our pipeline against a semi-automated<br />

region growing approach and manually annotated ground truth.<br />

Keywords<br />

pulmonary vessels, artery vein classification, airways<br />


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