Post-acquisition image based compensation for thickness variation in microscopy section series

TL;DR

This paper introduces an open-source method to estimate section thickness variations in serial electron microscopy images, improving volumetric reconstructions of neural tissue by addressing sectioning inconsistencies.

Contribution

The authors developed a novel signal-based approach to estimate relative z-positions of sections, enhancing accuracy in microscopy series analysis.

Findings

Promising results on ssTEM and FIB-SEM data

Open source plugins available for TrakEM2 and Fiji

Improves accuracy of 3D reconstructions in microscopy

Abstract

Serial section Microscopy is an established method for volumetric anatomy reconstruction. Section series imaged with Electron Microscopy are currently vital for the reconstruction of the synaptic connectivity of entire animal brains such as that of Drosophila melanogaster. The process of removing ultrathin layers from a solid block containing the specimen, however, is a fragile procedure and has limited precision with respect to section thickness. We have developed a method to estimate the relative z-position of each individual section as a function of signal change across the section series. First experiments show promising results on both serial section Transmission Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) series. We made our solution available as Open Source plugins for the TrakEM2 software and the ImageJ distribution Fiji.