BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images

TL;DR

This paper introduces an automated segmentation pipeline for the bronchovascular bundle in lung CT images, aiding pulmonary disease analysis and nodule segmentation with robustness across various imaging conditions.

Contribution

The proposed method offers a novel, invariant segmentation pipeline for bronchi and vessels in CT images, applicable to diverse patient conditions and imaging parameters.

Findings

Effective on both low-dose and standard-dose CT scans

Robust across different pathologies and imaging settings

Suitable for healthy and diseased lung studies

Abstract

Segmentation of the bronchovascular bundle within the lung parenchyma is a key step for the proper analysis and planning of many pulmonary diseases. It might also be considered the preprocessing step when the goal is to segment the nodules from the lung parenchyma. We propose a segmentation pipeline for the bronchovascular bundle based on the Computed Tomography images, returning either binary or labelled masks of vessels and bronchi situated in the lung parenchyma. The method consists of two modules, modeling of the bronchial tree and vessels. The core revolves around a similar pipeline, the determination of the initial perimeter by the GMM method, skeletonization, and hierarchical analysis of the created graph. We tested our method on both low-dose CT and standard-dose CT, with various pathologies, reconstructed with various slice thicknesses, and acquired from various machines. We conclude that the method is invariant with respect to the origin and parameters of the CT series. Our pipeline is best suited for studies with healthy patients, patients with lung nodules, and patients with emphysema.