SHREC Cryo-ET 2021 Dataset: Classification in Cryo-Electron Tomograms

<p>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.</p>

<p>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.</p>

<p>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.</p>

<p>You can find more details in <a href="https://diglib.eg.org/bitstream/handle/10.2312/3dor20211307/005-017.pdf?sequence=1&isAllowed=y">the related publication</a> and on the <a href="https://www.shrec.net/cryo-et">contest webpage</a>.</p>