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Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
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2017 (English)In: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Springer, 2017, Vol. 723, p. 182-194Conference paper, Published paper (Refereed)
Abstract [en]

Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 723, p. 182-194
Series
Communications in Computer and Information Science, ISSN 1865-0929 ; 723
Keyword [en]
Classification, Convolutional neural networks, CT angiography, Vascular skeleton
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-212015DOI: 10.1007/978-3-319-60964-5_16Scopus ID: 2-s2.0-85022182486ISBN: 9783319609638 OAI: oai:DiVA.org:kth-212015DiVA, id: diva2:1133363
Conference
21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Edinburgh, United Kingdom, 11 July 2017 through 13 July 2017
Funder
Swedish Research Council, 621-2014-6153
Note

QC 20170815

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2017-12-07Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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