Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling Article Swipe
Haoran Li
,
Xingjian Li
,
Jiahua Shi
,
Huaming Chen
,
Bo Du
,
Daisuke Kihara
,
Johan Barthélemy
,
Jun Shen
,
Min Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i1.32019
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i1.32019
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET image segmentation tasks remains challenging due to two main issues: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, directly collected from raw-data from the real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars. In contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this work, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on the improved Bilateral Filter to alleviate the domain shift problem. More importantly, we construct the first UDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v39i1.32019
- https://ojs.aaai.org/index.php/AAAI/article/download/32019/34174
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409346663
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409346663Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v39i1.32019Digital Object Identifier
- Title
-
Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-LabelingWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-11Full publication date if available
- Authors
-
Haoran Li, Xingjian Li, Jiahua Shi, Huaming Chen, Bo Du, Daisuke Kihara, Johan Barthélemy, Jun Shen, Min XuList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v39i1.32019Publisher landing page
- PDF URL
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https://ojs.aaai.org/index.php/AAAI/article/download/32019/34174Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/AAAI/article/download/32019/34174Direct OA link when available
- Concepts
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Artificial intelligence, Voxel, Segmentation, Pattern recognition (psychology), Computer science, Domain (mathematical analysis), Cryo-electron tomography, Computer vision, Mathematics, Medicine, Radiology, Tomography, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dataset | 194 |
| abstract_inverted_index.enhance | 67 |
| abstract_inverted_index.heavily | 36 |
| abstract_inverted_index.images. | 56 |
| abstract_inverted_index.imaging | 6 |
| abstract_inverted_index.issues: | 91 |
| abstract_inverted_index.labeled | 40 |
| abstract_inverted_index.leading | 153 |
| abstract_inverted_index.levels. | 121 |
| abstract_inverted_index.methods | 35, 78 |
| abstract_inverted_index.network | 70 |
| abstract_inverted_index.propose | 201 |
| abstract_inverted_index.remains | 85 |
| abstract_inverted_index.results | 235 |
| abstract_inverted_index.sector. | 32 |
| abstract_inverted_index.skills, | 46 |
| abstract_inverted_index.thereby | 47 |
| abstract_inverted_index.through | 98 |
| abstract_inverted_index.towards | 149 |
| abstract_inverted_index.usually | 96 |
| abstract_inverted_index.However, | 33, 75 |
| abstract_inverted_index.Vox-UDA, | 175 |
| abstract_inverted_index.adoption | 50 |
| abstract_inverted_index.applying | 76 |
| abstract_inverted_index.approach | 250 |
| abstract_inverted_index.compared | 251 |
| abstract_inverted_index.consists | 130 |
| abstract_inverted_index.contains | 100 |
| abstract_inverted_index.dataset, | 95, 109 |
| abstract_inverted_index.datasets | 243 |
| abstract_inverted_index.denoised | 203 |
| abstract_inverted_index.designed | 65 |
| abstract_inverted_index.directly | 79, 110 |
| abstract_inverted_index.existing | 34 |
| abstract_inverted_index.improved | 209 |
| abstract_inverted_index.interest | 28 |
| abstract_inverted_index.manually | 39 |
| abstract_inverted_index.methods. | 255 |
| abstract_inverted_index.multiple | 237 |
| abstract_inverted_index.obtained | 97 |
| abstract_inverted_index.problem. | 158, 217 |
| abstract_inverted_index.proposed | 249 |
| abstract_inverted_index.raw-data | 113 |
| abstract_inverted_index.requires | 43 |
| abstract_inverted_index.simulate | 188 |
| abstract_inverted_index.strategy | 205 |
| abstract_inverted_index.training | 128 |
| abstract_inverted_index.unknown, | 142 |
| abstract_inverted_index.(cryo-ET) | 2 |
| abstract_inverted_index.Bilateral | 210 |
| abstract_inverted_index.Extensive | 233 |
| abstract_inverted_index.alleviate | 213 |
| abstract_inverted_index.approach, | 173 |
| abstract_inverted_index.benchmark | 228 |
| abstract_inverted_index.collected | 111 |
| abstract_inverted_index.construct | 221 |
| abstract_inverted_index.contrast, | 135 |
| abstract_inverted_index.datasets. | 232 |
| abstract_inverted_index.hindering | 48 |
| abstract_inverted_index.introduce | 167 |
| abstract_inverted_index.scenario, | 117 |
| abstract_inverted_index.typically | 129 |
| abstract_inverted_index.unlabeled | 73 |
| abstract_inverted_index.Tomography | 1 |
| abstract_inverted_index.adaptation | 60, 172 |
| abstract_inverted_index.approaches | 21, 53, 62 |
| abstract_inverted_index.benchmarks | 238 |
| abstract_inverted_index.biological | 31 |
| abstract_inverted_index.generation | 185 |
| abstract_inverted_index.real-world | 116, 242 |
| abstract_inverted_index.structures | 14 |
| abstract_inverted_index.technology | 7 |
| abstract_inverted_index.volumetric | 19 |
| abstract_inverted_index.voxel-wise | 169 |
| abstract_inverted_index.widespread | 27 |
| abstract_inverted_index.adaptation. | 198 |
| abstract_inverted_index.challenges, | 162 |
| abstract_inverted_index.challenging | 86 |
| abstract_inverted_index.cross-noise | 196 |
| abstract_inverted_index.demonstrate | 244 |
| abstract_inverted_index.facilitates | 9 |
| abstract_inverted_index.near-atomic | 16 |
| abstract_inverted_index.performance | 71 |
| abstract_inverted_index.resolution. | 17 |
| abstract_inverted_index.simulation, | 99 |
| abstract_inverted_index.subtomogram | 179, 226 |
| abstract_inverted_index.superiority | 246 |
| abstract_inverted_index.target-like | 189 |
| abstract_inverted_index.experimental | 231, 234 |
| abstract_inverted_index.importantly, | 219 |
| abstract_inverted_index.incorporates | 182 |
| abstract_inverted_index.professional | 45 |
| abstract_inverted_index.segmentation | 20, 69, 83, 227 |
| abstract_inverted_index.specifically | 176 |
| abstract_inverted_index.unsupervised | 58, 170 |
| abstract_inverted_index.Additionally, | 199 |
| abstract_inverted_index.Cryo-Electron | 0 |
| abstract_inverted_index.segmentation. | 180 |
| abstract_inverted_index.unpredictable | 119 |
| abstract_inverted_index.macromolecular | 13 |
| abstract_inverted_index.macromolecules, | 152 |
| abstract_inverted_index.pseudo-labeling | 204 |
| abstract_inverted_index.fully-supervised | 52 |
| abstract_inverted_index.macromoleculars. | 133 |
| abstract_inverted_index.state-of-the-art | 253 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.5 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |