Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
School of Computer Science, University of Sydney, Australia.
J Struct Biol. 2023 Mar;215(1):107940. doi: 10.1016/j.jsb.2023.107940. Epub 2023 Jan 26.
Cryo-electron microscopy (cryo-EM) single-particle analysis is a revolutionary imaging technique to resolve and visualize biomacromolecules. Image alignment in cryo-EM is an important and basic step to improve the precision of the image distance calculation. However, it is a very challenging task due to high noise and low signal-to-noise ratio. Therefore, we propose a new deep unsupervised difference learning (UDL) strategy with novel pseudo-label guided learning network architecture and apply it to pair-wise image alignment in cryo-EM. The training framework is fully unsupervised. Furthermore, a variant of UDL called joint UDL (JUDL), is also proposed, which is capable of utilizing the similarity information of the whole dataset and thus further increase the alignment precision. Assessments on both real-world and synthetic cryo-EM single-particle image datasets suggest the new unsupervised joint alignment method can achieve more accurate alignment results. Our method is highly efficient by taking advantages of GPU devices. The source code of our methods is publicly available at "http://www.csbio.sjtu.edu.cn/bioinf/JointUDL/" for academic use.
冷冻电子显微镜（cryo-EM）单颗粒分析是一种革命性的成像技术，可用于解析和可视化生物大分子。在 cryo-EM 中，图像配准是提高图像距离计算精度的重要且基本的步骤。然而，由于噪声高、信噪比较低，这是一项极具挑战性的任务。因此，我们提出了一种新的深度无监督差分学习（UDL）策略，具有新颖的伪标签引导学习网络架构，并将其应用于 cryo-EM 中的成对图像配准。该训练框架完全是无监督的。此外，还提出了一种称为联合 UDL（JUDL）的 UDL 变体，它能够利用整个数据集的相似性信息，从而进一步提高配准精度。在真实和合成 cryo-EM 单颗粒图像数据集上的评估表明，新的无监督联合对齐方法可以实现更准确的对齐结果。我们的方法充分利用 GPU 设备的优势，效率很高。我们的方法的源代码可在“http://www.csbio.sjtu.edu.cn/bioinf/JointUDL/”上供学术使用。
