An Unsupervised Convolutional LSTM Network (C-LSTMNet) for Lung 4D-CT Registration Article Swipe
Hang Zhang
,
Hui Peng
,
Haipeng Xu
,
Fen Zhao
,
Yan-Chao Lou
,
Juan Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3396610
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3396610
This study proposed a novel method for lung Four-Dimensional Computed Tomography (4D-CT) deformable image registration (DIR) called convolutional long-short term memory network (C-LSTMNet). It allowed us to capture temporal and spatial features between current and previous phases, as well as perform groupwise registration for multiple image pairs in 4D-CT datasets. The proposed registration framework utilized a recurrent neural network (RNN) to accurately predict displacement vector fields (DVFs) between multiple image pairs in an end-to-end manner, taking into account the spatiotemporal relationship between phase images. The input consisted of a sequence of Three-Dimensional Computed Tomography (3D-CT) images captured from inspiratory phase to expiratory phase within a complete breathing cycle. In this sequence, the first phase image was defined as the target image, while the other phase images were considered as moving images. Multiple C-LSTM units were stacked together to capture temporal clues between these images. The proposed C-LSTMNet was trained using 85 collected 4D-CT datasets from lung cancer patients without supervision, and its efficiency was evaluated by employing an open-source 4D-CT dataset from dir-lab. Target registration error (TRE) was measured and compared between C-LSTMNet and 4 recently published registration methods in a control group. The mean and standard deviation of TRE in C-LSTMNet were mm, which outperformed other existing deep-learning methods in the control group in this study, and the calculation time for each forward prediction was about 0.45 seconds. The preliminary results on oncologic patients demonstrated that the proposed C-LSTMNet had the potential to accurately and quickly synchronize lung 4D-CT.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3396610
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10518039.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396605144
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396605144Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3396610Digital Object Identifier
- Title
-
An Unsupervised Convolutional LSTM Network (C-LSTMNet) for Lung 4D-CT RegistrationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Hang Zhang, Hui Peng, Haipeng Xu, Fen Zhao, Yan-Chao Lou, Juan YangList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3396610Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10518039.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10518039.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer science, Image registration, Convolutional neural network, Pattern recognition (psychology), Computer vision, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 87, 90, 198 |
| abstract_inverted_index.on | 237 |
| abstract_inverted_index.to | 26, 60, 100, 137, 248 |
| abstract_inverted_index.us | 25 |
| abstract_inverted_index.TRE | 199 |
| abstract_inverted_index.The | 50, 84, 144, 193, 234 |
| abstract_inverted_index.and | 29, 34, 160, 179, 183, 195, 222, 250 |
| abstract_inverted_index.for | 6, 43, 226 |
| abstract_inverted_index.had | 245 |
| abstract_inverted_index.its | 161 |
| abstract_inverted_index.mm, | 208 |
| abstract_inverted_index.the | 78, 111, 118, 122, 216, 223, 242, 246 |
| abstract_inverted_index.was | 115, 147, 163, 177, 230 |
| abstract_inverted_index.0.45 | 232 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.each | 227 |
| abstract_inverted_index.from | 97, 154, 171 |
| abstract_inverted_index.into | 76 |
| abstract_inverted_index.lung | 7, 155, 253 |
| abstract_inverted_index.mean | 194 |
| abstract_inverted_index.term | 19 |
| abstract_inverted_index.that | 241 |
| abstract_inverted_index.this | 109, 220 |
| abstract_inverted_index.time | 225 |
| abstract_inverted_index.well | 38 |
| abstract_inverted_index.were | 126, 134, 202 |
| abstract_inverted_index.(DIR) | 15 |
| abstract_inverted_index.(RNN) | 59 |
| abstract_inverted_index.(TRE) | 176 |
| abstract_inverted_index.0.87$ | 206 |
| abstract_inverted_index.4D-CT | 48, 152, 169 |
| abstract_inverted_index.about | 231 |
| abstract_inverted_index.clues | 140 |
| abstract_inverted_index.error | 175 |
| abstract_inverted_index.first | 112 |
| abstract_inverted_index.group | 218 |
| abstract_inverted_index.image | 13, 45, 69, 114 |
| abstract_inverted_index.input | 85 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.other | 123, 211 |
| abstract_inverted_index.pairs | 46, 70 |
| abstract_inverted_index.phase | 82, 99, 102, 113, 124 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.these | 142 |
| abstract_inverted_index.units | 133 |
| abstract_inverted_index.using | 149 |
| abstract_inverted_index.which | 209 |
| abstract_inverted_index.while | 121 |
| abstract_inverted_index.(DVFs) | 66 |
| abstract_inverted_index.4D-CT. | 254 |
| abstract_inverted_index.C-LSTM | 132 |
| abstract_inverted_index.Target | 173 |
| abstract_inverted_index.called | 16 |
| abstract_inverted_index.cancer | 156 |
| abstract_inverted_index.cycle. | 107 |
| abstract_inverted_index.fields | 65 |
| abstract_inverted_index.group. | 192 |
| abstract_inverted_index.image, | 120 |
| abstract_inverted_index.images | 95, 125 |
| abstract_inverted_index.memory | 20 |
| abstract_inverted_index.method | 5 |
| abstract_inverted_index.moving | 129 |
| abstract_inverted_index.neural | 57 |
| abstract_inverted_index.study, | 221 |
| abstract_inverted_index.taking | 75 |
| abstract_inverted_index.target | 119 |
| abstract_inverted_index.vector | 64 |
| abstract_inverted_index.within | 103 |
| abstract_inverted_index.(3D-CT) | 94 |
| abstract_inverted_index.(4D-CT) | 11 |
| abstract_inverted_index.account | 77 |
| abstract_inverted_index.allowed | 24 |
| abstract_inverted_index.between | 32, 67, 81, 141, 181 |
| abstract_inverted_index.capture | 27, 138 |
| abstract_inverted_index.control | 191, 217 |
| abstract_inverted_index.current | 33 |
| abstract_inverted_index.dataset | 170 |
| abstract_inverted_index.defined | 116 |
| abstract_inverted_index.forward | 228 |
| abstract_inverted_index.images. | 83, 130, 143 |
| abstract_inverted_index.manner, | 74 |
| abstract_inverted_index.methods | 188, 214 |
| abstract_inverted_index.network | 21, 58 |
| abstract_inverted_index.perform | 40 |
| abstract_inverted_index.phases, | 36 |
| abstract_inverted_index.predict | 62 |
| abstract_inverted_index.quickly | 251 |
| abstract_inverted_index.results | 236 |
| abstract_inverted_index.spatial | 30 |
| abstract_inverted_index.stacked | 135 |
| abstract_inverted_index.trained | 148 |
| abstract_inverted_index.without | 158 |
| abstract_inverted_index.Computed | 9, 92 |
| abstract_inverted_index.Multiple | 131 |
| abstract_inverted_index.captured | 96 |
| abstract_inverted_index.compared | 180 |
| abstract_inverted_index.complete | 105 |
| abstract_inverted_index.datasets | 153 |
| abstract_inverted_index.dir-lab. | 172 |
| abstract_inverted_index.existing | 212 |
| abstract_inverted_index.features | 31 |
| abstract_inverted_index.measured | 178 |
| abstract_inverted_index.multiple | 44, 68 |
| abstract_inverted_index.patients | 157, 239 |
| abstract_inverted_index.previous | 35 |
| abstract_inverted_index.proposed | 2, 51, 145, 243 |
| abstract_inverted_index.recently | 185 |
| abstract_inverted_index.seconds. | 233 |
| abstract_inverted_index.sequence | 89 |
| abstract_inverted_index.standard | 196 |
| abstract_inverted_index.temporal | 28, 139 |
| abstract_inverted_index.together | 136 |
| abstract_inverted_index.utilized | 54 |
| abstract_inverted_index.<tex-math | 204 |
| abstract_inverted_index.C-LSTMNet | 146, 182, 201, 244 |
| abstract_inverted_index.breathing | 106 |
| abstract_inverted_index.collected | 151 |
| abstract_inverted_index.consisted | 86 |
| abstract_inverted_index.datasets. | 49 |
| abstract_inverted_index.deviation | 197 |
| abstract_inverted_index.employing | 166 |
| abstract_inverted_index.evaluated | 164 |
| abstract_inverted_index.framework | 53 |
| abstract_inverted_index.groupwise | 41 |
| abstract_inverted_index.oncologic | 238 |
| abstract_inverted_index.potential | 247 |
| abstract_inverted_index.published | 186 |
| abstract_inverted_index.recurrent | 56 |
| abstract_inverted_index.sequence, | 110 |
| abstract_inverted_index.Tomography | 10, 93 |
| abstract_inverted_index.accurately | 61, 249 |
| abstract_inverted_index.considered | 127 |
| abstract_inverted_index.deformable | 12 |
| abstract_inverted_index.efficiency | 162 |
| abstract_inverted_index.end-to-end | 73 |
| abstract_inverted_index.expiratory | 101 |
| abstract_inverted_index.long-short | 18 |
| abstract_inverted_index.prediction | 229 |
| abstract_inverted_index.calculation | 224 |
| abstract_inverted_index.inspiratory | 98 |
| abstract_inverted_index.open-source | 168 |
| abstract_inverted_index.preliminary | 235 |
| abstract_inverted_index.synchronize | 252 |
| abstract_inverted_index.(C-LSTMNet). | 22 |
| abstract_inverted_index.demonstrated | 240 |
| abstract_inverted_index.displacement | 63 |
| abstract_inverted_index.outperformed | 210 |
| abstract_inverted_index.registration | 14, 42, 52, 174, 187 |
| abstract_inverted_index.relationship | 80 |
| abstract_inverted_index.supervision, | 159 |
| abstract_inverted_index.convolutional | 17 |
| abstract_inverted_index.deep-learning | 213 |
| abstract_inverted_index.spatiotemporal | 79 |
| abstract_inverted_index.<inline-formula> | 203 |
| abstract_inverted_index.Four-Dimensional | 8 |
| abstract_inverted_index.Three-Dimensional | 91 |
| abstract_inverted_index.notation="LaTeX">$1.30\pm | 205 |
| abstract_inverted_index.</tex-math></inline-formula> | 207 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.77392184 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |