Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology facilitating
the study of macromolecular structures at near-atomic resolution. Recent
volumetric segmentation approaches on cryo-ET images have drawn widespread
interest in 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 images segmentation tasks remains challenging
due to two main issues: 1) the source data, usually obtained through
simulation, contain a certain level of noise, while the target data, directly
collected from raw-data from real-world scenario, have unpredictable noise
levels. 2) the source data used for training typically consists of known
macromoleculars, while the target domain data are often unknown, causing the
model's segmenter to be biased towards these known macromolecules, leading to a
domain shift problem. To address these challenges, in this work, we introduce
the first 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 improved Bilateral Filter to alleviate the
domain shift problem. Experimental results on both simulated and real cryo-ET
subtomogram datasets demonstrate the superiority of our proposed approach
compared to state-of-the-art UDA methods.

本研究提出了第一种针对冷冻电镜亚原子体分割的基于体素的无监督领域自适应方法 Vox-UDA，通过噪声生成模块对源数据进行噪声模拟，以实现跨噪声级别自适应，并且提出了一种基于改进双边滤波的去噪伪标记策略来缓解领域漂移问题，实验证明了该方法相对于最先进的无监督领域自适应方法的优越性。