MIST: Microscopy Image Stitching Tool

Summary form only given. Motivation: Automated microscopy enables scientists to image an area of an experimental sample that is much larger than the microscope's Field of View (FOV) and to carry out time-lapse studies of cell cultures. An automated microscope acquires these images by generating a grid of partially overlapping images. This process generates hundreds to hundreds of thousands of image tiles that need to be stitched into a wide image. We address the problem of creating image mosaics from a grid of overlapping tiles constrained to only translational offsets. The challenges of creating a large mosaic image are: (1) sensitivity to image features in the overlapping regions of adjacent tiles (e.g., during the early period of cell colony growth), (2) computational requirements needed to assemble the resulting mosaic image, and (3) absence of ground truth needed for evaluating the accuracy of a stitching method. Results: This paper describes a stitching method called MIST (Microscopy Image Stitching Tool) with minimized translational uncertainty for large collections of grid-based microscopy tiles. The method improves tile translations computed using a registration method, such as the Fourier transform based phase correlation, by optimizing the normalized cross correlation between the overlap of adjacent tiles. The optimization incorporates mechanical properties of a microscope stage to filter translations with high errors. We estimate the microscope stage repeatability from the computed translations of the grid-based image tiles and then improve all translations using constrained Hill Climbing restricted to searching a square area of 4 times the stage repeatability per side. We also present a methodology for evaluating stitching accuracy based on creating reference centroid distance and area measurements of regions of interests that fit inside one FOV. The regions of interests (ROI) are segmented first and their mutual centroid distances and areas are measured using the microscope stage coordinates. The stitching accuracy is quantified by comparing the reference measurements to the measurements obtained by stitching a set of grid-based tiles by means of four NIST -derived metrics: false positive (added ROIs), false negative (undetected ROI), centroid distance error and area error. Following this methodology, we prepared three large reference datasets of stem cell colonies with low colony seeding which result in high uncertainty associated with the translation offsets. MIST generated a stitched image with an average colony centroid distance error less than 2 % that of a field of view and an average area error of 5 %. The sources of these errors include mechanical uncertainties, sample photobleaching, segmentation and stitching. We also show that the area error is mainly due to photobleaching and not stitching. We compared MIST stitching to the top five popular methods used in the literature. MIST produced the most accurate stitching result among all methods. Conclusions: MIST is an accurate stitching tool that can be applied to grid-based tiles with unknown translational offsets. Its performance-oriented implementation yields a fast execution time that makes the algorithm suitable for creating large mosaics (up to TBs in size). The evaluation methodology for stitching accuracy along with NIST -derived four performance metrics provides a general approach to characterize stitching algorithm performance. The application of the methodology in our case generated three reusable reference datasets with cell colonies. Availability: MIST is available as a Matlab executable or an ImageJ plugin. MIST ImageJ plugin has a CPU and a GPU implementation. All the information regarding this tool and its source code can be found at the following link: https://isg.nist. gov/.