SHREC Cryo-ET 2020 Dataset: Classification in Cryo-Electron Tomograms

With the advent of the direct electron detectors and the associated resolution revolution, cryo-electron tomography (cryo-ET) has the potential to bridge this gap by simultaneously visualizing the cellular architecture and structural details of macromolecular assemblies, thee-dimensionally. The technique offers insights in key cellular processes and opens new possibilities for rational drug design.

However, the biological samples are radiation sensitive, which limits the maximal resolution and signal-to-noise ratio. Innovation in computational methods remains key to derive biological information from the tomograms. To promote such innovation, we organize this SHREC track and provide a simulated dataset with the goal of establishing a benchmark in localization and classification of biological particles in cryo-electron tomograms.