There is a noticeable gap in knowledge about the organization of cellular life at the mesoscopic level. 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. The publicly available dataset contains ten reconstructed tomograms obtained from a simulated cell-like volume. Each volume contains twelve different types of proteins, varying in size and structure. Participants had access to 9 out of 10 of the cell-like ground-truth volumes for learning-based methods, and had to predict protein class and location in the test tomogram.

You can find more details in the related publication and on the contest webpage.