AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

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 twofold: 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. In recent years, the development of new electron microscopy technologies allowed the automated serial imaging of entire biological specimens, from cells to model organisms. Amongst these techniques, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a preferred technology for gathering serial images at isotropic resolution. After volumetric acquisition, an important step for proper visualization and accurate morphometric analysis is the alignment of the image stack along the z-axis. However, due to the size and complexity of the data, alignments using simple translations are most commonly used 1,2 instead of adapting the transformations to the specific type of data. Consequently, the most common algorithms used to find correlation between adjacent slices are alignment by SIFT 3 and alignment using a template structure matched by cross correlation, also known as template matching (TM). In the specific case of data acquired using the Atlas 5 software 4 , TM is efficiently performed on markings created at the surface of the sample (Fig. 1a). These markings are at a constant position with respect to the flat sample surface. In case of SIFT when only global translations are applied, each slice is aligned to the previous, preserving local morphological properties (a few slices) along the z-axis, while disturbing the global shape of objects across long distances. As a consequence, straight objects, such as the sample surface plane, can become crooked in a non-predictable manner (Fig. 2a). Additionally, for FIB-SEM data, slices can project distorted images when different parts of an imaged cross-section are exposed to different rates of radiation. This effect typically...

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