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Related Experiment Video

Updated: Apr 8, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Optimizing parameters of an open-source airway segmentation algorithm using different CT images.

Pietro Nardelli¹, Kashif A Khan², Alberto Corvò³

¹School of Engineering , University College Cork, College Road, Cork, Ireland. p.nardelli@umail.ucc.ie.

Biomedical Engineering Online

|June 27, 2015

Summary

This summary is machine-generated.

This study introduces a reliable, semi-automatic airway segmentation algorithm for CT scans, demonstrating consistent performance across various imaging parameters and providing an open-source platform for lung disease assessment.

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Area of Science:

Medical Imaging
Pulmonary Medicine
Computer-Aided Diagnosis

Background:

Computed tomography (CT) is crucial for diagnosing lung diseases.
Accurate airway segmentation from CT images aids in lung disease assessment.
Existing airway segmentation methods lack reliability across diverse CT scan parameters.

Purpose of the Study:

To develop a simple, reliable, semi-automatic airway segmentation algorithm for CT images.
To optimize the algorithm for consistent performance across various CT acquisition parameters.
To establish an open-source platform for airway segmentation and evaluation.

Main Methods:

A region-growing approach is used for segmenting tracheal and bronchial anatomy.
Independent segmentation of trachea, right, and left bronchi with distinct thresholds.
Optimization and validation on clinical cases, EXACT'09 data, and a lung phantom with varied CT parameters.

Main Results:

Successful segmentation across all tested cases with minimal leakage.
Comparable results to existing methods on clinical data.
Demonstrated reliability and stability across multiple CT platforms and acquisition parameters, with slice thickness being the most influential factor.

Conclusions:

The developed system is the first open-source airway segmentation platform.
The quantitative evaluation method offers a repeatable tool for comparing segmentation platforms.
The algorithm is stable across CT platforms and acquisition parameters, serving as a foundation for advanced airway segmentation.