Fully automated 2D–3D registration and verification

Varnavas, Andreas; Carrell, Tom; Penney, Graeme

doi:10.1016/j.media.2015.08.005

Cited by 50Medical Image Segmentation TechniquesMedical Imaging and AnalysisAdvanced Radiotherapy Techniques

Fully automated 2D–3D registration and verification

Varnavas, Andreas, Carrell, Tom, Penney, Graeme·2015·Medical Image Analysis

DOI 10.1016/j.media.2015.08.005

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Abstract

Clinical application of 2D-3D registration technology often requires a significant amount of human interaction during initialisation and result verification. This is one of the main barriers to more widespread clinical use of this technology. We propose novel techniques for automated initial pose estimation of the 3D data and verification of the registration result, and show how these techniques can be combined to enable fully automated 2D-3D registration, particularly in the case of a vertebra based system. The initialisation method is based on preoperative computation of 2D templates over a wide range of 3D poses. These templates are used to apply the Generalised Hough Transform to the intraoperative 2D image and the sought 3D pose is selected with the combined use of the generated accumulator arrays and a Gradient Difference Similarity Measure. On the verification side, two algorithms are proposed: one using normalised features based on the similarity value and the other based on the pose agreement between multiple vertebra based registrations. The proposed methods are employed here for CT to fluoroscopy registration and are trained and tested with data from 31 clinical procedures with 417 low dose, i.e. low quality, high noise interventional fluoroscopy images. When similarity value based verification is used, the fully automated system achieves a 95.73% correct registration rate, whereas a no registration result is produced for the remaining 4.27% of cases (i.e. incorrec…

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Feature-based 2D/3D registration methods require features or anatomical landmarks that are specific to every object and must be selected with domain knowledge and be clearly visible in target images.
“However, these features and anatomical landmarks are specific to every object and must be selected with domain knowledge and such that they are clearly visible in the target images.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [10]

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Varnavas, A., Carrell, T. W. G., & Penney, G. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2227337 · Cited by 34 · see citing works
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Fassnacht C.: An approach to 2D/3D registration of a vertebra in 2D x-ray fluoroscopies with 3D CT images
Weese, J., Buzug, T. M., Cristian Lorenz, A., & Fassnacht, C. (1997)
An approach to 2D/3D registration of a vertebra in 2D x-ray fluoroscopies with 3D CT images
Weese, J., Buzug, T. M., Cristian Lorenz, A., & Fassnacht, C. (1997) · Lecture notes in computer science · doi:10.1007/bfb0029231 · Cited by 58 · see citing works
2D-3D registration of CT vertebra volume to fluoroscopy projection: A calibration model assessment (doi:10.1155/2010/806094)
Bifulco, P., Cesarelli, M., Allen, R., Romano, M., Fratini, A., & Pasquariello, G. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2010/806094

Authors: Varnavas, A., Carrell, T., & Penney, G.
Affiliations: St Thomas' Hospital (GB)
Corresponding author: Varnavas, Andreas
Venue: Medical Image Analysis
Publisher: Elsevier BV
Published: 2015-12
Volume: 26
Issue: 1
Pages: 108-119
ISSN: 1361-8415 (ISSN-L)
DOI: 10.1016/j.media.2015.08.005
OpenAlex: W2166490563
Cited by: 50
References: 26
Citations by year: 2026: 5 · 2025: 1 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 2 · 2020: 9 · 2019: 4 · 2018: 4 · 2017: 2 · 2016: 3
FWCI: 2.60
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Medical Image Analysis (publishedVersion)
PubMed (publishedVersion)
Research Portal (King's College London) (submittedVersion)
Funders: National Institute for Health Research
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Keywords: 2D-3D registration, automatic registration, initial pose estimation, low-dose fluoroscopy, interventional fluoroscopy
MeSH terms: Algorithms, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Subtraction Technique, Tomography, X-Ray Computed, Reproducibility of Results, Imaging, Three-Dimensional, Multimodal Imaging

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Fully automated 2D–3D registration and verification

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