AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks
SCIENTIFIC REPORTS(2020)
摘要
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.
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关键词
Data processing,Image processing,Machine learning,Software,Science,Humanities and Social Sciences,multidisciplinary
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