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

Hdl Handle:
http://hdl.handle.net/10147/574922
Title:
Optimizing parameters of an open-source airway segmentation algorithm using different CT images
Authors:
Nardelli, Pietro; Khan, Kashif A; Corvò, Alberto; Moore, Niamh; Murphy, Mary J; Twomey, Maria; O’Connor, Owen J; Kennedy, Marcus P; Estépar, Raúl S J; Maher, Michael M; Cantillon-Murphy, Pádraig
Citation:
BioMedical Engineering OnLine. 2015 Jun 26;14(1):62
Issue Date:
26-Jun-2015
URI:
http://dx.doi.org/10.1186/s12938-015-0060-2; http://hdl.handle.net/10147/574922
Abstract:
Abstract Background 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.
Language:
en
Keywords:
COMPUTED TOMOGRAPHY; LUNG DISEASE

Full metadata record

DC FieldValue Language
dc.contributor.authorNardelli, Pietroen
dc.contributor.authorKhan, Kashif Aen
dc.contributor.authorCorvò, Albertoen
dc.contributor.authorMoore, Niamhen
dc.contributor.authorMurphy, Mary Jen
dc.contributor.authorTwomey, Mariaen
dc.contributor.authorO’Connor, Owen Jen
dc.contributor.authorKennedy, Marcus Pen
dc.contributor.authorEstépar, Raúl S Jen
dc.contributor.authorMaher, Michael Men
dc.contributor.authorCantillon-Murphy, Pádraigen
dc.date.accessioned2015-08-17T15:11:42Zen
dc.date.available2015-08-17T15:11:42Zen
dc.date.issued2015-06-26en
dc.identifier.citationBioMedical Engineering OnLine. 2015 Jun 26;14(1):62en
dc.identifier.urihttp://dx.doi.org/10.1186/s12938-015-0060-2en
dc.identifier.urihttp://hdl.handle.net/10147/574922en
dc.description.abstractAbstract Background 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.language.isoenen
dc.subjectCOMPUTED TOMOGRAPHYen
dc.subjectLUNG DISEASEen
dc.titleOptimizing parameters of an open-source airway segmentation algorithm using different CT imagesen
dc.language.rfc3066enen
dc.rights.holderNardelli et al.en
dc.date.updated2015-08-14T13:19:05Zen
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