Registration of transmission electron images via minimizing landmark correspondence displacement

Saalfeld et al have a cool new paper (here) explaining their algorithm for registration (i.e. proper stacking) of serial section transmission electron microscopy images. Microscopy instruments alone are too imprecise for seamless stitching of the images, leaving some images rotated and out of place. So, computer tech is needed to help for automation.

Their algorithm extracts landmarks (e.g. “blobs”), from all section micrographs (“tiles”), identifies landmark correspondences between tiles, and estimates the tile configuration that minimizes (via the function arg min) the sum of all the squared correspondence displacements between landmarks. In the demonstration below, feature candidates are circled, with size proportional to the feature’s scale. On bottom, two correspondence matches are shown as an example:

False matches = white, true matches = black; doi:10.1093/bioinformatics/btq219

They tested their algorithm on 6000+ Drosophila larval brain 60 nm sections imaged at 4.68 nm per pixel resolution. They found that their reconstruction yields continuity of structures such as axon bundles within and across image sections. They conclude that “globally optimal reconstruction of entire brains on TEM level will enable registration of 3D light microscopy data onto electron microscopy volumes. By that it will be possible to establish the connection between brain macro (neuronal lineages) and micro (synaptic connectivity) circuitry.” Exciting stuff.

Reference

Saalfeld S, et al. 2010 As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets. Bioinformatics. doi:10.1093/bioinformatics/btq219.