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Cited by 91Medical Image Segmentation TechniquesAdvanced Vision and Imaging3D Shape Modeling and Analysis

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Deformable 2D-3D Registration of Vascular Structures in a One View Scenario

Groher, M., Zikic, D., Navab, N.·2009·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2008.2011519
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Abstract

Alignment of angiographic 3D scans to 2D projections is an important issue for 3D depth perception and navigation during interventions. Currently, in a setting where only one 2D projection is available, methods employing a rigid transformation model present the state of the art for this problem. In this work, we introduce a method capable of deformably registering 3D vessel structures to a respective single projection of the scene. Our approach addresses the inherent ill-posedness of the problem by incorporating a priori knowledge about the vessel structures into the formulation. We minimize the distance between the 2D points and corresponding projected 3D points together with regularization terms encoding the properties of length preservation of vessel structures and smoothness of deformation. We demonstrate the performance and accuracy of the proposed method by quantitative tests on synthetic examples as well as real angiographic scenes.

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  • Other research employs 3D-2D registration incorporating a non-rigid transformation, parameterized using prior information or properties of length preservation of vessels.

    “Other research reported in the literature employs 3D–2D registration incorporating a non-rigid transformation. For example, the transformation can be parameterized using prior information such as the principal components and eigen-values of motion derived from a training dataset (Wang et al 2013) or properties of length preservation of vessels (Groher et al 2009).”

    From Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation (2013) · cites this paper as Groher et al 2009

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References 47 · Cited by 91

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Metadata

Authors
Groher, M., Zikic, D., & Navab, N.
Affiliations
Technical University of Munich (DE)
ORCID
Navab, N.
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2009-06
Volume
28
Issue
6
Pages
847-860
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2008.2011519
OpenAlex
W2133606340
Cited by
91
References
47
Citations by year
2026: 2 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 5 · 2020: 6 · 2019: 5 · 2018: 2 · 2017: 5 · 2016: 6 · 2015: 9 · 2014: 5 · 2013: 14 · 2012: 8
FWCI
6.98
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
Keywords
2D-3D registration, deformable image registration, vascular architecture, angiography, 3D vessel reconstruction
MeSH terms
Algorithms, Angiography, Artificial Intelligence, Blood Vessels, Carcinoma, Hepatocellular, Humans, Image Processing, Computer-Assisted, Liver, Models, Cardiovascular, Models, Statistical

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