SHREC 2021: Classification in cryo-electron tomograms
This work addresses the need for standardized benchmarks in cryo-ET analysis, benefiting researchers in computational biology, but it is incremental as it focuses on dataset creation and comparative evaluation rather than introducing new methods.
The authors tackled the problem of benchmarking computational methods for localizing and classifying biological macromolecules in cryo-electron tomograms by generating a novel simulated dataset and evaluating seven methods, showing that learning-based approaches outperform traditional template matching with notable performance gains.
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.