Pose-Dependent Weights and Domain Randomization for Fully Automatic X-Ray to CT Registration Article Swipe
Matthias Grimm
,
Javier Esteban
,
Mathias Unberath
,
Nassir Navab
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2021.3073815
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2021.3073815
Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work adresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real Xrays. Then, for each patient CT, a patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained networks predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (+-standard deviation) target registration error in millimetres is 4.1 +- 4.3 for simulated X-rays with a success rate of 92% and 4.2 +- 3.9 for real X-rays with a success rate of 86.8%, where a success is defined as a translation error of less than 30mm.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmi.2021.3073815
- OA Status
- green
- Cited By
- 1
- References
- 22
- Related Works
- 19
- OpenAlex ID
- https://openalex.org/W3098238965
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3098238965Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tmi.2021.3073815Digital Object Identifier
- Title
-
Pose-Dependent Weights and Domain Randomization for Fully Automatic X-Ray to CT RegistrationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-16Full publication date if available
- Authors
-
Matthias Grimm, Javier Esteban, Mathias Unberath, Nassir NavabList of authors in order
- Landing page
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https://doi.org/10.1109/tmi.2021.3073815Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2011.07294Direct OA link when available
- Concepts
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Initialization, Computer science, Landmark, Artificial intelligence, Weighting, Inference, Cluster analysis, Set (abstract data type), Range (aeronautics), Computer vision, Pattern recognition (psychology), Algorithm, Composite material, Materials science, Radiology, Programming language, MedicineTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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19Other works algorithmically related by OpenAlex
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