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

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dc.contributor.authorNardelli, Pietro
dc.contributor.authorKhan, Kashif A.
dc.contributor.authorCorvo, Alberto
dc.contributor.authorMoore, Niamh
dc.contributor.authorMurphy, Mary J.
dc.contributor.authorTwomey, Maria
dc.contributor.authorO'Connor, Owen J.
dc.contributor.authorKennedy, Marcus P.
dc.contributor.authorEstepar, Raul San Jose
dc.contributor.authorMaher, Michael M.
dc.contributor.authorCantillon-Murphy, Pádraig
dc.contributor.funderHealth Research Boarden
dc.date.accessioned2018-08-14T14:04:31Z
dc.date.available2018-08-14T14:04:31Z
dc.date.issued2015-06-26
dc.date.updated2018-06-20T15:15:01Z
dc.description.abstractBackground: Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters. Methods: In this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT’09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered. Results: All the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams’ methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation. Conclusion: The system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm.en
dc.description.sponsorshipHealth Research Board, Ireland (POR/2012/31)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationNardelli, P., Khan, K. A., Corvò, A., Moore, N., Murphy, M. J., Twomey, M., O’Connor, O. J., Kennedy, M. P., Estépar, R. S. J., Maher, M. M. and Cantillon-Murphy, P. (2015) 'Optimizing parameters of an open-source airway segmentation algorithm using different CT images', BioMedical Engineering OnLine, 14(1), 62 (24pp). doi: 10.1186/s12938-015-0060-2en
dc.identifier.doi10.1186/s12938-015-0060-2
dc.identifier.endpage62-24en
dc.identifier.issn1475-925X
dc.identifier.issued14en
dc.identifier.journaltitleBioMedical Engineering OnLineen
dc.identifier.startpage62-1en
dc.identifier.urihttps://hdl.handle.net/10468/6608
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urihttps://doi.org/10.1186/s12938-015-0060-2
dc.rights© 2015 Nardelli et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectAirway segmentationen
dc.subjectRegion growingen
dc.subjectComputed tomography (CT)en
dc.subject3D Sliceren
dc.subjectITKen
dc.subjectImage processingen
dc.subjectLungen
dc.subjectInsight Segmentation and Registration Toolkit (ITK)en
dc.titleOptimizing parameters of an open-source airway segmentation algorithm using different CT images.en
dc.typeArticle (peer-reviewed)en
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