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.
Highlights
For Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) data, slices can project distorted images when different parts of an imaged cross-section are exposed to different rates of radiation
We evaluated TM, a common alignment strategy for FIB-SEM, which relies on marks introduced into the platinum layer deposited at the surface of the sample to protect it upon acquisition (Fig. 1a)
The interest for ultrastructural volume imaging is growing in Biology
Summary
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 occurs during auto-focus and auto-stigmation operations, which are triggered periodically during a run The most recent one applied to serial section transmission electron microscopy (ssTEM), uses elastic deformations in an optimized manner along the z-stack[7] One drawback of this method is that local deformations can become very severe which does not ensure maintenance of morphological properties throughout the image data (Supplementary Fig. S1, Video S1). To overcome the alignment induced drift effect as well as misaligned regions not adjacent to the sample surface, we introduce a novel alignment scheme which we call Alignment to Median Smoothed Template (AMST) This method first creates a template dataset by pre-aligning the stack of images with either TM or SIFT and by smoothing it along the z-direction using a median filter. We demonstrate that better alignment enables a high segmentation quality, which we illustrate by means of an organelle boundary prediction using a CNN
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