AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks
| Autor: | Anna M. Steyer, Nicole L. Schieber, Julian Hennies, José Miguel Serra Lleti, Rachel M. Templin, Yannick Schwab |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Scanning electron microscope Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION lcsh:Medicine Scale-invariant feature transform 02 engineering and technology Convolutional neural network Focused ion beam Article 03 medical and health sciences Image processing Machine learning Median filter Segmentation Computer vision lcsh:Science Multidisciplinary Pixel business.industry Template matching lcsh:R 021001 nanoscience & nanotechnology Data processing 030104 developmental biology lcsh:Q Artificial intelligence Affine transformation 0210 nano-technology business Software |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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