Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

Deniz Aykac; Eric A. Huffman; Geoffrey McLennan; Joseph M. Reinhardt

doi:10.1109/TMI.2003.815905

Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

Deniz Aykac, Eric A. Huffman, Geoffrey McLennan, Joseph M. Reinhardt

Multimodal Sensor Analytics

Research output: Contribution to journal › Article › peer-review

212 Scopus citations

Abstract

The lungs exchange air with the external environment via the pulmonary airways. Computed tomography (CT) scanning can be used to obtain detailed images of the pulmonary anatomy, including the airways. These images have been used to measure airway geometry, study airway reactivity, and guide surgical interventions. Prior to these applications, airway segmentation can be used to identify the airway lumen in the CT images. Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming. We describe a fully automatic technique for segmenting the airway tree in three-dimensional (3-D) CT images of the thorax. We use grayscale morphological reconstruction to identify candidate airways on CT slices and then reconstruct a connected 3-D airway tree. After segmentation, we estimate airway branchpoints based on connectivity changes in the reconstructed tree. Compared to manual analysis on 3-mm-thick electron-beam CT images, the automatic approach has an overall, airway branch detection sensitivity of approximately 73%.

Original language	English
Pages (from-to)	940-950
Number of pages	11
Journal	IEEE Transactions on Medical Imaging
Volume	22
Issue number	8
DOIs	https://doi.org/10.1109/TMI.2003.815905
State	Published - Aug 2003

Funding

Manuscript received December 5, 2002; revised January 10, 2003. This work was supported in part by a Biomedical Engineering Research Grant from the Whitaker Foundation, in part by a CAREER Award from the National Science Foundation (NSF), and in part by the National Institutes of Health (NIH) under Grant HL64368-01 and Grant HL60158-02. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was P. Cinquin. Asterisk indicates corresponding author.

Funders	Funder number
National Science Foundation
National Institutes of Health	HL60158-02, HL64368-01
National Heart, Lung, and Blood Institute	R01HL060158
Whitaker Foundation

Keywords

Airway segmentation
Pulmonary imaging
Volumetric imaging
X-ray CT

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TMI.2003.815905

Cite this

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title = "Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images",

abstract = "The lungs exchange air with the external environment via the pulmonary airways. Computed tomography (CT) scanning can be used to obtain detailed images of the pulmonary anatomy, including the airways. These images have been used to measure airway geometry, study airway reactivity, and guide surgical interventions. Prior to these applications, airway segmentation can be used to identify the airway lumen in the CT images. Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming. We describe a fully automatic technique for segmenting the airway tree in three-dimensional (3-D) CT images of the thorax. We use grayscale morphological reconstruction to identify candidate airways on CT slices and then reconstruct a connected 3-D airway tree. After segmentation, we estimate airway branchpoints based on connectivity changes in the reconstructed tree. Compared to manual analysis on 3-mm-thick electron-beam CT images, the automatic approach has an overall, airway branch detection sensitivity of approximately 73%.",

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AB - The lungs exchange air with the external environment via the pulmonary airways. Computed tomography (CT) scanning can be used to obtain detailed images of the pulmonary anatomy, including the airways. These images have been used to measure airway geometry, study airway reactivity, and guide surgical interventions. Prior to these applications, airway segmentation can be used to identify the airway lumen in the CT images. Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming. We describe a fully automatic technique for segmenting the airway tree in three-dimensional (3-D) CT images of the thorax. We use grayscale morphological reconstruction to identify candidate airways on CT slices and then reconstruct a connected 3-D airway tree. After segmentation, we estimate airway branchpoints based on connectivity changes in the reconstructed tree. Compared to manual analysis on 3-mm-thick electron-beam CT images, the automatic approach has an overall, airway branch detection sensitivity of approximately 73%.

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Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

Abstract

Funding

Keywords

UN SDGs

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