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Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface

Overview of attention for article published in Biomechanics and Modeling in Mechanobiology, October 2016
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Title
Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface
Published in
Biomechanics and Modeling in Mechanobiology, October 2016
DOI 10.1007/s10237-016-0838-6
Pubmed ID
Authors

Shinjiro Miyawaki, Merryn H. Tawhai, Eric A. Hoffman, Sally E. Wenzel, Ching-Long Lin

Abstract

We propose a method to construct three-dimensional airway geometric models based on airway skeletons, or centerlines (CLs). Given a CT-segmented airway skeleton and surface, the proposed CL-based method automatically constructs subject-specific models that contain anatomical information regarding branches, include bifurcations and trifurcations, and extend from the trachea to terminal bronchioles. The resulting model can be anatomically realistic with the assistance of an image-based surface; alternatively a model with an idealized skeleton and/or branch diameters is also possible. This method systematically identifies and classifies trifurcations to successfully construct the models, which also provides the number and type of trifurcations for the analysis of the airways from an anatomical point of view. We applied this method to 16 normal and 16 severe asthmatic subjects using their computed tomography images. The average distance between the surface of the model and the image-based surface was 11 % of the average voxel size of the image. The four most frequent locations of trifurcations were the left upper division bronchus, left lower lobar bronchus, right upper lobar bronchus, and right intermediate bronchus. The proposed method automatically constructed accurate subject-specific three-dimensional airway geometric models that contain anatomical information regarding branches using airway skeleton, diameters, and image-based surface geometry. The proposed method can construct (i) geometry automatically for population-based studies, (ii) trifurcations to retain the original airway topology, (iii) geometry that can be used for automatic generation of computational fluid dynamics meshes, and (iv) geometry based only on a skeleton and diameters for idealized branches.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 18%
Researcher 3 11%
Professor > Associate Professor 3 11%
Professor 2 7%
Student > Postgraduate 2 7%
Other 6 21%
Unknown 7 25%
Readers by discipline Count As %
Engineering 10 36%
Design 2 7%
Mathematics 1 4%
Agricultural and Biological Sciences 1 4%
Energy 1 4%
Other 3 11%
Unknown 10 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 December 2017.
All research outputs
#16,069,695
of 23,849,058 outputs
Outputs from Biomechanics and Modeling in Mechanobiology
#285
of 486 outputs
Outputs of similar age
#205,018
of 322,469 outputs
Outputs of similar age from Biomechanics and Modeling in Mechanobiology
#4
of 11 outputs
Altmetric has tracked 23,849,058 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 486 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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 322,469 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.