Microscopy Image Stitching Tool (MIST), is a stitching tool for 2D grids of images. MIST estimates the stage mechanical model (actuator backlash, stage repeatability ‘r’, etc.) from computed pairwise translations and then minimizes stitching errors by optimizing the translations within a (4r)^2 square area. This minimizes the maximum uncertainty related to the translation computation for any pair of images.
With new microscope technologies, scientists are acquiring terabyte-sized datasets to cover large area of their experiments. An automated optical microscope acquires images of a cell cultures specimen. The microscope acquires a grid of partially overlapping images because the microscope field of view is much smaller than the dimensions of the specimen being imaged. This process generates hundreds of thousands of images that need to be stitched into a large mosaic to derive meaningful information. Some of these large mosaics are sparse, mainly at the beginning of an experiment before cells grow and cover more areas. This creates overlapping areas with no foreground pixel intensities to compute the translations between tiles, which increases the translation computation uncertainty for the entire mosaic. Moreover, stitching such large image mosaic is computationally taxing; this becomes overwhelming for live cell experiments.
J. Chalfoun, M. Majurski, T. Blattner, W. Keyrouz, P. Bajcsy, and M. C. Brady, “MIST: Accurate and Scalable Microscopy Image Stitching Method with Stage Modeling and Error Minimization,” Scientific Reports, vol. 7, no. 1, 2017, DOI: 10.1038/s41598-017-04567-y
MIST has been applied to large and diverse datasets in terms of content, imaging modalities, and microscopes; totaling over a thousand fully stitched images. The stitched datasets have been acquired using five microscopes (Leica, Olympus, Nikon, Focused Ion Beam Scanning Electron Microscope (FIB-SEM), and Zeiss), four different imaging modalities (fluorescent, phase contrast, bright-field, and FIB-SEM), a range of image overlaps, and a large range of grid sizes (5x5 to 300x300).
2D Grid of Images + Time Series + Multiple Image Modalities
MIST was designed to perform 2D time-sequence multichannel stitching. It cannot perform volumetric stitching (3 spatial dimensions) which requires a system to identify and correlate features across a third dimension (e.g., Z-axis). However, MIST can stitch a numbered series of independent image grids, for example, a time-series of image grids. Additionally, MIST also handles datasets that can use the same image positions, such as stitching multiple channels, using the assemble from metadata functionality.
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