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Abstract

Alignment of stacks of serial images generated by focused ion Beam Scanning electron Microscopy
(FIB-SEM) is generally performed using translations only, either through slice-by-slice alignments
with SIFT or alignment by template matching. However, limitations of these methods are two-fold:
the introduction of a bias along the dataset in the z-direction which seriously alters the morphology
of observed organelles and a missing compensation for pixel size variations inherent to the image
acquisition itself. These pixel size variations result in local misalignments and jumps of a few
nanometers in the image data that can compromise downstream image analysis. We introduce a
novel approach which enables affine transformations to overcome local misalignments while avoiding
the danger of introducing a scaling, rotation or shearing trend along the dataset. Our method first
computes a template dataset with an alignment method restricted to translations only. This pre-aligned
dataset is then smoothed selectively along the z-axis with a median filter, creating a template to which
the raw data is aligned using affine transformations. Our method was applied to FIB-SEM datasets and
showed clear improvement of the alignment along the z-axis resulting in a significantly more accurate
automatic boundary segmentation using a convolutional neural network.