Evaluation of CT Image Synthesis Methods:From Atlas-based Registration to Deep Learning Article Swipe
Andreas D. Lauritzen
,
Xenophon Papademetris
,
Sergei Turovets
,
John A. Onofrey
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1906.04467
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1906.04467
Computed tomography (CT) is a widely used imaging modality for medical diagnosis and treatment. In electroencephalography (EEG), CT imaging is necessary for co-registering with magnetic resonance imaging (MRI) and for creating more accurate head models for the brain electrical activity due to better representation of bone anatomy. Unfortunately, CT imaging exposes patients to potentially harmful sources of ionizing radiation. Image synthesis methods present a solution for avoiding extra radiation exposure. In this paper, we perform image synthesis to create a realistic, synthetic CT image from MRI of the same subject, and we present a comparison of different image synthesis techniques. Using a dataset of 30 paired MRI and CT image volumes, our results compare image synthesis using deep neural network regression, state-of-the-art adversarial deep learning, as well as atlas-based synthesis utilizing image registration. We also present a novel synthesis method that combines multi-atlas registration as a prior to deep learning algorithms, in which we perform a weighted addition of synthetic CT images, derived from atlases, to the output of a deep neural network to obtain a residual type of learning. In addition to evaluating the quality of the synthetic CT images, we also demonstrate that image synthesis methods allow for more accurate bone segmentation using the synthetic CT imaging than would otherwise be possible by segmenting the bone in the MRI directly.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1906.04467
- https://arxiv.org/pdf/1906.04467
- OA Status
- green
- Cited By
- 2
- References
- 16
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2951951606
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2951951606Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1906.04467Digital Object Identifier
- Title
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Evaluation of CT Image Synthesis Methods:From Atlas-based Registration to Deep LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-11Full publication date if available
- Authors
-
Andreas D. Lauritzen, Xenophon Papademetris, Sergei Turovets, John A. OnofreyList of authors in order
- Landing page
-
https://arxiv.org/abs/1906.04467Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1906.04467Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1906.04467Direct OA link when available
- Concepts
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Artificial intelligence, Deep learning, Computer science, Image registration, Segmentation, Atlas (anatomy), Magnetic resonance imaging, Image synthesis, Image quality, Medical imaging, Artificial neural network, Computer vision, Pattern recognition (psychology), Image (mathematics), Radiology, Medicine, AnatomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2020: 2Per-year citation counts (last 5 years)
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16Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.more | 31, 200 |
| abstract_inverted_index.same | 88 |
| abstract_inverted_index.than | 209 |
| abstract_inverted_index.that | 140, 194 |
| abstract_inverted_index.this | 71 |
| abstract_inverted_index.type | 177 |
| abstract_inverted_index.used | 6 |
| abstract_inverted_index.well | 126 |
| abstract_inverted_index.with | 23 |
| abstract_inverted_index.(MRI) | 27 |
| abstract_inverted_index.Image | 59 |
| abstract_inverted_index.Using | 100 |
| abstract_inverted_index.allow | 198 |
| abstract_inverted_index.brain | 37 |
| abstract_inverted_index.extra | 67 |
| abstract_inverted_index.image | 75, 83, 97, 109, 114, 131, 195 |
| abstract_inverted_index.novel | 137 |
| abstract_inverted_index.prior | 146 |
| abstract_inverted_index.using | 116, 204 |
| abstract_inverted_index.which | 152 |
| abstract_inverted_index.would | 210 |
| abstract_inverted_index.(EEG), | 16 |
| abstract_inverted_index.better | 42 |
| abstract_inverted_index.create | 78 |
| abstract_inverted_index.method | 139 |
| abstract_inverted_index.models | 34 |
| abstract_inverted_index.neural | 118, 171 |
| abstract_inverted_index.obtain | 174 |
| abstract_inverted_index.output | 167 |
| abstract_inverted_index.paired | 105 |
| abstract_inverted_index.paper, | 72 |
| abstract_inverted_index.widely | 5 |
| abstract_inverted_index.compare | 113 |
| abstract_inverted_index.dataset | 102 |
| abstract_inverted_index.derived | 162 |
| abstract_inverted_index.exposes | 50 |
| abstract_inverted_index.harmful | 54 |
| abstract_inverted_index.images, | 161, 190 |
| abstract_inverted_index.imaging | 7, 18, 26, 49, 208 |
| abstract_inverted_index.medical | 10 |
| abstract_inverted_index.methods | 61, 197 |
| abstract_inverted_index.network | 119, 172 |
| abstract_inverted_index.perform | 74, 154 |
| abstract_inverted_index.present | 62, 92, 135 |
| abstract_inverted_index.quality | 185 |
| abstract_inverted_index.results | 112 |
| abstract_inverted_index.sources | 55 |
| abstract_inverted_index.Computed | 0 |
| abstract_inverted_index.accurate | 32, 201 |
| abstract_inverted_index.activity | 39 |
| abstract_inverted_index.addition | 157, 181 |
| abstract_inverted_index.anatomy. | 46 |
| abstract_inverted_index.atlases, | 164 |
| abstract_inverted_index.avoiding | 66 |
| abstract_inverted_index.combines | 141 |
| abstract_inverted_index.creating | 30 |
| abstract_inverted_index.ionizing | 57 |
| abstract_inverted_index.learning | 149 |
| abstract_inverted_index.magnetic | 24 |
| abstract_inverted_index.modality | 8 |
| abstract_inverted_index.patients | 51 |
| abstract_inverted_index.possible | 213 |
| abstract_inverted_index.residual | 176 |
| abstract_inverted_index.solution | 64 |
| abstract_inverted_index.subject, | 89 |
| abstract_inverted_index.volumes, | 110 |
| abstract_inverted_index.weighted | 156 |
| abstract_inverted_index.diagnosis | 11 |
| abstract_inverted_index.different | 96 |
| abstract_inverted_index.directly. | 221 |
| abstract_inverted_index.exposure. | 69 |
| abstract_inverted_index.learning, | 124 |
| abstract_inverted_index.learning. | 179 |
| abstract_inverted_index.necessary | 20 |
| abstract_inverted_index.otherwise | 211 |
| abstract_inverted_index.radiation | 68 |
| abstract_inverted_index.resonance | 25 |
| abstract_inverted_index.synthesis | 60, 76, 98, 115, 129, 138, 196 |
| abstract_inverted_index.synthetic | 81, 159, 188, 206 |
| abstract_inverted_index.utilizing | 130 |
| abstract_inverted_index.comparison | 94 |
| abstract_inverted_index.electrical | 38 |
| abstract_inverted_index.evaluating | 183 |
| abstract_inverted_index.radiation. | 58 |
| abstract_inverted_index.realistic, | 80 |
| abstract_inverted_index.segmenting | 215 |
| abstract_inverted_index.tomography | 1 |
| abstract_inverted_index.treatment. | 13 |
| abstract_inverted_index.adversarial | 122 |
| abstract_inverted_index.algorithms, | 150 |
| abstract_inverted_index.atlas-based | 128 |
| abstract_inverted_index.demonstrate | 193 |
| abstract_inverted_index.multi-atlas | 142 |
| abstract_inverted_index.potentially | 53 |
| abstract_inverted_index.regression, | 120 |
| abstract_inverted_index.techniques. | 99 |
| abstract_inverted_index.registration | 143 |
| abstract_inverted_index.segmentation | 203 |
| abstract_inverted_index.registration. | 132 |
| abstract_inverted_index.Unfortunately, | 47 |
| abstract_inverted_index.co-registering | 22 |
| abstract_inverted_index.representation | 43 |
| abstract_inverted_index.state-of-the-art | 121 |
| abstract_inverted_index.electroencephalography | 15 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |