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Undergraduate Research Showcase

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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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