Reconstruction by direct labeling in electron tomography, using Gibbs priors and a marginal posterior mode approach

We propose a new direct labeling approach to electron tomography of biological macromolecules, using Gibbs priors and the marginal posterior mode (MPM) estimator. The aim is to produce a tessellation of space into small voxels and label each voxel as containing ice, protein, or ribosomal nucleic acid (RNA), based on only a few electron micrographs. We show that the MPM estimator outperforms the maximum a posteriori probability (MAP) estimator, which has been the standard not only for this type of application but also for most computer vision problems.