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。

SHREC 2021: 冷冻电子断层扫描的分类