A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation
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BiBTeXSegmenting the blood pool and myocardium from a 3D cardiovascular magnetic resonance (CMR) image allows to create a patient-specific heart model for surgical planning in children with complex congenital heart disease (CHD). Implementation of semi-automatic or automatic segmentation algorithms is challenging because of a high anatomical variability of the heart defects, low contrast, and intensity variations in the images. Therefore, manual segmentation is the gold standard but it is labor-intensive. In this paper we report the set-up and results of a highly scalable semi-automatic diffusion algorithm for image segmentation. The method extrapolates the information from a small number of expert manually labeled reference slices to the remaining volume. While results of most semi-automatic algorithms strongly depend on well-chosen but usually unknown parameters this approach is parameter-free. Validation is performed on twenty 3D CMR images.
SEEK ID: https://publications.h-its.org/publications/240
DOI: 10.1007/978-3-319-52280-7_12
Research Groups: Data Mining and Uncertainty Quantification
Publication type: InCollection
Journal: Reconstruction, Segmentation, and Analysis of Medical Images
Book Title: Reconstruction, Segmentation, and Analysis of Medical Images, RAMBO 2016 and HVSMR 2016, Athens, Greece, October 17, 2016
Editors: Zuluaga, Maria A. and Bhatia, Kanwal and Kainz, Bernhard and Moghari, Mehdi H. and Pace, Danielle F.
Publisher: Lecture Notes in Computer Science Springer International Publishing
Citation: Reconstruction, Segmentation, and Analysis of Medical Images 10129:121-128,Springer International Publishing
Date Published: 2017
Registered Mode: imported from a bibtex file
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Created: 7th Sep 2019 at 10:40
Last updated: 5th Mar 2024 at 21:23
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Research Groups: Data Mining and Uncertainty Quantification
Institutions: Heidelberg Institute for Theoretical Studies
Research Groups: Data Mining and Uncertainty Quantification
Institutions: Heidelberg Institute for Theoretical Studies
https://orcid.org/0000-0003-0506-1171
Research Groups: Scientific Databases and Visualisation, Astroinformatics, Data Mining and Uncertainty Quantification, Groups and Geometry, Natural Language Processing, Computational Molecular Evolution, Computational Carbon Chemistry, Physics of Stellar Objects, Playground, Molecular and Cellular Modeling, Stellar Evolution Theory, SIMPLAIX
Institutions: Heidelberg Institute for Theoretical Studies
Public web page: Not specified
