Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis from Low-Dose CT Scans

Juerg Tschirren; Eric A Hoffman; Geoffrey McLennan; Milan Sonka

doi:10.1109/TMI.2005.857654

Back

Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis from Low-Dose CT Scans

Journal article

Open access

Peer reviewed

Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis from Low-Dose CT Scans

Juerg Tschirren, Eric A Hoffman, Geoffrey McLennan and Milan Sonka

IEEE transactions on medical imaging, Vol.24(12), pp.1529-1539

12/2005

DOI: 10.1109/TMI.2005.857654

PMCID: PMC1851666

PMID: 16353370

Files and links (1)

url

https://www.ncbi.nlm.nih.gov/pmc/articles/1851666View

Open Access

Abstract

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that sub-voxel accuracy is achieved for all airway sizes and airway orientations.

adaptive region of interest

fuzzy connectivity

pulmonary imaging

Airway tree segmentation

quantitative analysis

Details

Title: Subtitle: Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis from Low-Dose CT Scans
Creators: Juerg Tschirren - J. Tschirren was with the Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
Eric A Hoffman - Eric A. Hoffman is with the University of Iowa, Dept. of Radiology, Iowa City, IA, USA
Geoffrey McLennan - Geoffrey McLennan is with the University of Iowa, Dept. of Internal aMedicien and Radiology, Iowa City, IA, USA
Milan Sonka - Milan Sonka is with the Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA (email
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.24(12), pp.1529-1539
DOI: 10.1109/TMI.2005.857654
PMID: 16353370
PMCID: PMC1851666
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 12/2005
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9984046935702771

Metrics

21 Record Views

212 Times Cited - Web of Science

See more details