Structural Analysis
A reconstructed tomogram is noisy and distorted by the missing wedge, so individual molecules are rarely clear. Structural analysis turns these volumes into interpretable structure and biology: raising signal-to-noise by subtomogram averaging, locating particles by template matching or neural picking, and segmenting membranes and organelles.
Each subtomogram is the same signal plus independent noise. Averaging N copies leaves the signal intact while the noise standard deviation falls as 1/√N — which is how subtomogram averaging recovers near-atomic structure from copies that are individually very noisy.
Articles in this base 3 articles
Subtomogram averaging
Averaging many copies of a repeating particle in 3D raises signal-to-noise and pushes tomography toward sub-nanometer resolution.
Particle picking & template matching
Locating copies of a target molecule in a noisy tomogram is the step that precedes subtomogram averaging.
Segmentation
Delineating membranes, organelles, filaments, and macromolecules turns a tomogram into an interpretable map of cellular structures.