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

Overview of attention for article published in BioMedical Engineering OnLine, June 2015
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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3 X users

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Title
Optimizing parameters of an open-source airway segmentation algorithm using different CT images
Published in
BioMedical Engineering OnLine, June 2015
DOI 10.1186/s12938-015-0060-2
Pubmed ID
Authors

Pietro Nardelli, Kashif A Khan, Alberto Corvò, Niamh Moore, Mary J Murphy, Maria Twomey, Owen J O’Connor, Marcus P Kennedy, Raúl San José Estépar, Michael M Maher, Pádraig Cantillon-Murphy

Abstract

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. 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. 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. 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.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Portugal 1 1%
China 1 1%
Unknown 67 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Student > Master 12 17%
Researcher 7 10%
Student > Bachelor 6 9%
Lecturer 5 7%
Other 14 20%
Unknown 13 19%
Readers by discipline Count As %
Engineering 22 31%
Medicine and Dentistry 14 20%
Computer Science 9 13%
Agricultural and Biological Sciences 2 3%
Mathematics 2 3%
Other 6 9%
Unknown 15 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2015.
All research outputs
#7,462,560
of 22,815,414 outputs
Outputs from BioMedical Engineering OnLine
#207
of 824 outputs
Outputs of similar age
#89,095
of 263,581 outputs
Outputs of similar age from BioMedical Engineering OnLine
#6
of 20 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,581 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.