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Vessel Segmentation Using Implicit Model-Guided Level Sets
Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0002-0442-3524
Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0001-5765-2964
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes an automatic segmentation method of vasculature that combines level-sets with an implicit 3D model of the vessels. First, a 3D vessel model from a set of initial centerlines is generated. This model is incorporated in the level set propagation to regulate the growth of the vessel contour. After evolving the level set, new centerlines are extracted and the diameter of vessels is re-estimated in order to generate a new vessel model. The propagation and re-modeling steps are repeated until convergence. The organizers of the 3D Cardiovascular Imaging: a MICCAI segmentation challenge report the following results for the 24 testing datasets. The sensitivity and PPV are 0.26, 0.40 for QCA and 0.05 and 0.22 for CTA. As for quantitation, the absolute and RMS dierences for QCA are 29.7% and 34.1% and the weighted kappa for CTA are -0.37. As for lumen segmentation, the dice are 0.68 and 0.69 for healthy and diseased vessel segments respectively. Performance for QCA and lumen segmentation are close to the reported by the organizers for three human observers.

Place, publisher, year, edition, pages
2012.
Keyword [en]
Level sets, computed tomography angiography, generalized cylinders
National Category
Cardiac and Cardiovascular Systems Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-89844OAI: oai:DiVA.org:liu-89844DiVA: diva2:609939
Conference
MICCAI Workshop "3D Cardiovascular Imaging: a MICCAI segmentation Challenge", Nice France, 1st of October 2012.
Funder
Swedish Heart Lung Foundation, 2011-0376Swedish Research Council, 2011-5197
Available from: 2013-03-13 Created: 2013-03-08 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

fulltext(2540 kB)