Post-acquisition image based compensation for thickness variation in microscopy section series
This addresses a specific bottleneck in serial section microscopy for researchers in neuroscience and anatomy, enabling more accurate 3D reconstructions of brain connectivity, though it appears incremental as it builds on existing imaging techniques.
The paper tackles the problem of thickness variation in microscopy section series, which is crucial for volumetric anatomy reconstruction, by developing a method to estimate the relative z-position of each section based on signal changes, with initial experiments showing promising results on ssTEM and FIB-SEM data.
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.