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

Nardelli P, Khan KA, Corvò A, Moore N, Murphy MJ, Twomey M, O'Connor OJ, Kennedy MP, Estépar RS, Maher MM, Cantillon-Murphy P - Biomed Eng Online (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering , University College Cork, College Road, Cork, Ireland. p.nardelli@umail.ucc.ie.

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.

No MeSH data available.


Related in: MedlinePlus

Leakage scoring scheme as presented to clinical experts. A score of 5 is given to an image with significant leakage presence, while 1 represents an image with no leakage.
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Fig6: Leakage scoring scheme as presented to clinical experts. A score of 5 is given to an image with significant leakage presence, while 1 represents an image with no leakage.

Mentions: Leakage presence is the most important parameter to be considered once airway is segmented. This often turns out to be a complicated task, as it may be difficult to distinguish a small leakage from a correctly segmented branch. For this reason, a new leakage evaluation system has been implemented for the presented work; four expert clinicians from the field of respiratory medicine or radiology were instructed on what leakage is. They were then asked to analyze the 3-D reconstructed model of the airway as well as the label placed on the chest CT image and to score the segmentation ranging from 1 to 5, where 5 was a segmentation presenting significant leakage and 1 was an image with no leakage. Figure 6 shows the scoring scheme presented to the clinicians in order to score the images. Average scores were then used to evaluate the segmentation.Figure 5


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

Nardelli P, Khan KA, Corvò A, Moore N, Murphy MJ, Twomey M, O'Connor OJ, Kennedy MP, Estépar RS, Maher MM, Cantillon-Murphy P - Biomed Eng Online (2015)

Leakage scoring scheme as presented to clinical experts. A score of 5 is given to an image with significant leakage presence, while 1 represents an image with no leakage.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4482101&req=5

Fig6: Leakage scoring scheme as presented to clinical experts. A score of 5 is given to an image with significant leakage presence, while 1 represents an image with no leakage.
Mentions: Leakage presence is the most important parameter to be considered once airway is segmented. This often turns out to be a complicated task, as it may be difficult to distinguish a small leakage from a correctly segmented branch. For this reason, a new leakage evaluation system has been implemented for the presented work; four expert clinicians from the field of respiratory medicine or radiology were instructed on what leakage is. They were then asked to analyze the 3-D reconstructed model of the airway as well as the label placed on the chest CT image and to score the segmentation ranging from 1 to 5, where 5 was a segmentation presenting significant leakage and 1 was an image with no leakage. Figure 6 shows the scoring scheme presented to the clinicians in order to score the images. Average scores were then used to evaluate the segmentation.Figure 5

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering , University College Cork, College Road, Cork, Ireland. p.nardelli@umail.ucc.ie.

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.

No MeSH data available.


Related in: MedlinePlus