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。

Vox-UDA: 面向冷冻电镜亚体积分割的体素级无监督领域适应与去噪伪标签

Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron  Subtomogram Segmentation with Denoised Pseudo Labeling

Understanding the structures of biological macromolecules is highly important
as they are closely associated with cellular functionalities. Comprehending the
precise organization actin filaments is crucial because they form the dynamic
cytoskeleton, which offers structural support to cells and connects the cell's
interior with its surroundings. However, determining the precise organization
of actin filaments is challenging due to the poor quality of cryo-electron
tomography (cryo-ET) images, which suffer from low signal-to-noise (SNR) ratios
and the presence of missing wedge, as well as diverse shape characteristics of
actin filaments. To address these formidable challenges, the primary component
of this dissertation focuses on developing sophisticated computational
techniques for tracing actin filaments. In particular, three novel
methodologies have been developed: i) BundleTrac, for tracing bundle-like actin
filaments found in Stereocilium, ii) Spaghetti Tracer, for tracing filaments
that move individually with loosely cohesive movements, and iii) Struwwel
Tracer, for tracing randomly orientated actin filaments in the actin network.
The second component of the dissertation introduces a convolutional neural
network (CNN) based segmentation model to determine the location of protein
secondary structures, such as helices and beta-sheets, in medium-resolution
(5-10 Angstrom) 3-dimensional cryo-electron microscopy (cryo-EM) images. This
methodology later evolved into a tool named DeepSSETracer. The final component
of the dissertation presents a novel algorithm, cylindrical fit measure, to
estimate image structure match at helix regions in medium-resolution cryo-EM
images. Overall, my dissertation has made significant contributions to
addressing critical research challenges in structural biology by introducing
various computational methods and tools.

理解生物大分子的结构对于细胞功能密切相关至关重要，特别是准确理解肌动蛋白纤维的组织是非常关键的，而这是因为它们形成了动态的细胞骨架，对细胞提供结构支持并连接其内部与周围环境。为了解决肌动蛋白纤维的精确组织问题，本文介绍了三种新的计算方法，并引入了用于确定蛋白质二级结构位置的卷积神经网络 (CNN) 模型和用于估计中分辨率冷冻电子显微镜图像中螺旋区域图像结构相似性的新算法。

三维冷冻 ET/EM 密度图像中的分子图案追踪和分割通过算法图像处理和深度学习技术

Tracing and segmentation of molecular patterns in 3-dimensional  cryo-et/em density maps through algorithmic image processing and deep  learning-based techniques

Cryo-electron tomography (cryo-ET) is an imaging technique that allows
three-dimensional visualization of macro-molecular assemblies under near-native
conditions. Cryo-ET comes with a number of challenges, mainly low
signal-to-noise and inability to obtain images from all angles. Computational
methods are key to analyze cryo-electron tomograms.
To promote innovation in computational methods, we generate a novel simulated
dataset to benchmark different methods of localization and classification of
biological macromolecules in tomograms. Our publicly available dataset contains
ten tomographic reconstructions of simulated cell-like volumes. Each volume
contains twelve different types of complexes, varying in size, function and
structure.
In this paper, we have evaluated seven different methods of finding and
classifying proteins. Seven research groups present results obtained with
learning-based methods and trained on the simulated dataset, as well as a
baseline template matching (TM), a traditional method widely used in cryo-ET
research. We show that learning-based approaches can achieve notably better
localization and classification performance than TM. We also experimentally
confirm that there is a negative relationship between particle size and
performance for all methods.

本文介绍了为了促进计算方法的创新，生成了一个新的模拟数据集，用于评估在 cryo - 电子断层扫描中蛋白质的定位和分类的不同方法。我们评估了七种不同的方法，包括基于学习的方法和传统方法，结果表明学习方法比传统方法具有更好的定位和分类性能，并且所有方法的性能与粒子大小之间存在负相关关系。