Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling
Li, Haoran Li, Xingjian Shi, Jiahua Chen, Huaming Du, Bo Kihara, Daisuke Barthelemy, Johan Shen, Jun Xu, Min
Li, Haoran
Li, Xingjian
Shi, Jiahua
Chen, Huaming
Du, Bo
Kihara, Daisuke
Barthelemy, Johan
Shen, Jun
Xu, Min
Author
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
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.
Citation
H. Li et al., “Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 1, pp. 406–414, Apr. 2025, doi: 10.1609/AAAI.V39I1.32019
Source
Proceedings of the AAAI Conference on Artificial Intelligence
Conference
AAAI Conference on Artificial Intelligence
Keywords
Subjects
Source
AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence