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Automated vessel segmentation in dual energy computed tomography data of the pelvis and lower extremities

Aim: To evaluate the clinical feasibility of a newly developed, fully automatic vessel segmentation software with automatic structured bone elimination (ASBE) using graph-matching and subvoxel analysis. Materials and Methods: Dual energy computed tomography angiography (DECTA) data of 108 vessel seg...

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Bibliographic Details
Main Authors: Kostrzewa, Michael (Author) , Rathmann, Nils-Andreas (Author) , Hesser, Jürgen (Author) , Schönberg, Stefan (Author) , Diehl, Steffen J. (Author)
Format: Article (Journal)
Language:English
Published: September-October 2016
In: In vivo
Year: 2016, Volume: 30, Issue: 5, Pages: 651-655
ISSN:1791-7549
Online Access:Verlag, Volltext: http://iv.iiarjournals.org/content/30/5/651
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Author Notes:Michael Kostrzewa, Nils Rathmann, Jürgen Hesser, Kurt Huck, Stefan O. Schönberg and Steffen J. Diehl
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Summary:Aim: To evaluate the clinical feasibility of a newly developed, fully automatic vessel segmentation software with automatic structured bone elimination (ASBE) using graph-matching and subvoxel analysis. Materials and Methods: Dual energy computed tomography angiography (DECTA) data of 108 vessel segments were evaluated using the ASBE software and a commercial software against the digital subtraction angiography (DSA) standard of reference. Results: Using the ASBE software, sensitivity increased from 87.1% to 96.8% and data concordance with DSA increased from 64.5% to 88.6%, whereas specificity slightly decreased (79.2% vs. 87%) compared to the commercial software. Data concordance between ASBE software and DSA was especially high in severely stenosed (grade of stenosis >75%) blood vessels. Conclusion: ASBE showed good concordance with the DSA standard of reference and non-inferiority compared to the commercial segmentation software. The main advantage of the ASBE software lies in its full automation and, thus, lower susceptibility to user prone errors.
Item Description:Gesehen am 14.01.2019
Physical Description:Online Resource
ISSN:1791-7549

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