Pose-dependent weights and Domain Randomization for fully automatic\n X-ray to CT Registration Article Swipe
Matthias Grimm
,
Javier Esteban
,
Mathias Unberath
,
Nassir Navab
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.07294
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.07294
Fully automatic X-ray to CT registration requires a solid initialization to\nprovide an initial alignment within the capture range of existing\nintensity-based registrations. This work adresses that need by providing a\nnovel automatic initialization, which enables end to end registration. First, a\nneural network is trained once to detect a set of anatomical landmarks on\nsimulated X-rays. A domain randomization scheme is proposed to enable the\nnetwork to overcome the challenge of being trained purely on simulated data and\nrun inference on real Xrays. Then, for each patient CT, a patient-specific\nlandmark extraction scheme is used. It is based on backprojecting and\nclustering the previously trained networks predictions on a set of simulated\nX-rays. Next, the network is retrained to detect the new landmarks. Finally the\ncombination of network and 3D landmark locations is used to compute the\ninitialization using a perspective-n-point algorithm. During the computation of\nthe pose, a weighting scheme is introduced to incorporate the confidence of the\nnetwork in detecting the landmarks. The algorithm is evaluated on the pelvis\nusing both real and simulated x-rays. The mean (+-standard deviation) target\nregistration error in millimetres is 4.1 +- 4.3 for simulated X-rays with a\nsuccess rate of 92% and 4.2 +- 3.9 for real X-rays with a success rate of\n86.8%, where a success is defined as a translation error of less than 30mm.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2011.07294
- https://arxiv.org/pdf/2011.07294
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287598236
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287598236Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2011.07294Digital Object Identifier
- Title
-
Pose-dependent weights and Domain Randomization for fully automatic\n X-ray to CT RegistrationWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-11-14Full publication date if available
- Authors
-
Matthias Grimm, Javier Esteban, Mathias Unberath, Nassir NavabList of authors in order
- Landing page
-
https://arxiv.org/abs/2011.07294Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2011.07294Direct link to full text PDF
- 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), Computation, Computer vision, Algorithm, Pattern recognition (psychology), Medicine, Radiology, Composite material, Materials science, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.previously | 95 |
| abstract_inverted_index.(+-standard | 165 |
| abstract_inverted_index.computation | 133 |
| abstract_inverted_index.incorporate | 142 |
| abstract_inverted_index.millimetres | 170 |
| abstract_inverted_index.predictions | 98 |
| abstract_inverted_index.to\nprovide | 10 |
| abstract_inverted_index.translation | 202 |
| abstract_inverted_index.registration | 5 |
| abstract_inverted_index.the\nnetwork | 60, 146 |
| abstract_inverted_index.on\nsimulated | 50 |
| abstract_inverted_index.pelvis\nusing | 157 |
| abstract_inverted_index.randomization | 54 |
| abstract_inverted_index.registration. | 36 |
| abstract_inverted_index.backprojecting | 92 |
| abstract_inverted_index.initialization | 9 |
| abstract_inverted_index.registrations. | 20 |
| abstract_inverted_index.and\nclustering | 93 |
| abstract_inverted_index.initialization, | 30 |
| abstract_inverted_index.the\ncombination | 115 |
| abstract_inverted_index.simulated\nX-rays. | 103 |
| abstract_inverted_index.perspective-n-point | 129 |
| abstract_inverted_index.the\ninitialization | 126 |
| abstract_inverted_index.target\nregistration | 167 |
| abstract_inverted_index.existing\nintensity-based | 19 |
| abstract_inverted_index.patient-specific\nlandmark | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.23316498 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |