Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time.

低温电子断层扫描（cryo-ET）生成细胞组织的3D可视化，使生物学家能够以纳米分辨率分析处于近乎自然状态的细胞结构。近来，深度学习方法已证明在可通过cryo-ET捕获的大分子结构的分类和分段中具有良好的性能，但是训练单个深度学习模型需要大量手动标记和分段的先前观察到的数据。为了在野外进行分类和分段（即，在训练数据有限且类别不明的情况下），需要开发新颖的深度学习模型以对冷冻-ET捕获的看不见的大分子进行分类和分段。在本文中，我们开发了一个单次学习框架，称为cryo-ET单次网络（COS-Net），可以同时对大分子结构进行分类并生成体素级3D分割，每个类别仅使用一个训练样本。我们对22个大分子类别的实验结果表明，我们的COS-Net可以有效地对少量样品的大分子结构进行分类，并同时产生准确的3D分割。