Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts

Christian Bauer; Michael Eberlein; Reinhard R Beichel

doi:10.1109/TMI.2014.2374615

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Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts

Journal article

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Peer reviewed

Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts

Christian Bauer, Michael Eberlein and Reinhard R Beichel

IEEE transactions on medical imaging, Vol.34(5), pp.1063-1076

05/2015

DOI: 10.1109/TMI.2014.2374615

PMCID: PMC4417425

PMID: 25438305

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https://www.ncbi.nlm.nih.gov/pmc/articles/4417425View

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Abstract

We present a graph-based framework for airway tree reconstruction from computerized tomography (CT) scans and evaluate the performance of different feature categories and their combinations on five lung cohorts. The approach consists of two main processing steps. First, potential airway branch and connection candidates are identified and represented by a graph structure with weighted nodes and edges, respectively. Second, an optimization algorithm is utilized for generating an airway detection result by selecting a subset of airway branches and connections based on graph weights derived from image features. The performance of the algorithm with different feature categories and their combinations was assessed on a set of 50 lung CT scans from five different cohorts, including normal and diseased lungs. Results show trade-offs between feature categories/combinations in terms of correctly (true positive) and incorrectly (false positive) identified airways. Also, the performance of features in dependence of lung cohort was analyzed. Across all cohorts, a good trade-off with high true positive rate (TPR) and low false positive rate (FPR) was achieved by a combination of gray-value, local shape, and structural features. This combination enabled extracting 91.80% of reference airways (TPR) in combination with a low FPR of 1.00%. In addition, this variant was evaluated on the public EXACT'09 test set, and a comparison with other airway detection approaches is provided. One of the main advantages of the presented method is that it is robust against local disturbances/artifacts or other ambiguities that are frequently occurring in lung CT scans.

graph-based optimization

Image segmentation

Computed tomography

Lungs

X-ray computed tomography

Cavity resonators

Airway detection

Image reconstruction

Optimization

Diseases

Details

Title: Subtitle: Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts
Creators: Christian Bauer - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Michael Eberlein - Dept. of Internal Med., Univ. of Iowa Carver Coll. of Med., Iowa City, IA, USA
Reinhard R Beichel - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.34(5), pp.1063-1076
DOI: 10.1109/TMI.2014.2374615
PMID: 25438305
PMCID: PMC4417425
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Grant note: UL1TR000442 / Institute for Clinical and Translational Science, University of Iowa R01HL111453 / (NIH/NHLBI)
Language: English
Date published: 05/2015
Academic Unit: Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; Internal Medicine
Record Identifier: 9984083268902771

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