  Serial section electron microscopy (ssEM) is a widely used technique for
obtaining volumetric information of biological tissues at nanometer scale.
However, accurate 3D reconstructions of identified cellular structures and
volumetric quantifications require precise estimates of section thickness and
anisotropy (or stretching) along the XY imaging plane. In fact, many image
processing algorithms simply assume isotropy within the imaging plane. To
ameliorate this problem, we present a method for estimating thickness and
stretching of electron microscopy sections using non-parametric Bayesian
regression of image statistics. We verify our thickness and stretching
estimates using direct measurements obtained by atomic force microscopy (AFM)
and show that our method has a lower estimation error compared to a recent
indirect thickness estimation method as well as a relative Z coordinate
estimation method. Furthermore, we have made the first dataset of ssSEM images
with directly measured section thickness values publicly available for the
evaluation of indirect thickness estimation methods.
