↓ Skip to main content

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
Altmetric Badge

About this Attention Score

  • Among the highest-scoring outputs from this source (#45 of 353)
  • Good Attention Score compared to outputs of the same age (66th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
62 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Portugal 1 2%
China 1 2%
Unknown 59 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 21%
Student > Ph. D. Student 12 19%
Student > Bachelor 6 10%
Professor > Associate Professor 5 8%
Researcher 4 6%
Other 13 21%
Unknown 9 15%
Readers by discipline Count As %
Engineering 21 34%
Medicine and Dentistry 12 19%
Computer Science 10 16%
Mathematics 2 3%
Agricultural and Biological Sciences 1 2%
Other 5 8%
Unknown 11 18%

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
#1,323,895
of 5,276,531 outputs
Outputs from BioMedical Engineering OnLine
#45
of 353 outputs
Outputs of similar age
#58,593
of 186,466 outputs
Outputs of similar age from BioMedical Engineering OnLine
#3
of 24 outputs
Altmetric has tracked 5,276,531 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 353 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done well, scoring higher than 84% 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 186,466 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 66% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.